[et_pb_section fb_built=”1″ _builder_version=”4.27.5″ _module_preset=”default” custom_margin=”||||false|false” custom_padding=”||||false|false” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.27.5″ _module_preset=”default” custom_margin=”||||false|false” custom_margin_tablet=”||||false|false” custom_margin_phone=”||||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”0px||||false|false” global_colors_info=”{}” custom_margin__hover_enabled=”off|desktop”][et_pb_column type=”1_2″ _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.5″ _module_preset=”default” text_font=”||||||||” header_font=”New Science Medium||||||||” header_2_font=”||||||||” global_colors_info=”{}”]<\/p>\n
Juvo<\/span><\/li><\/ol><\/nav>[\/et_pb_text][et_pb_text _builder_version=”4.27.5″ _module_preset=”default” text_font=”||||||||” global_colors_info=”{}”]<\/p>\nData engineering is a field that works with data to find meaning, make sense of it, and ultimately extract value from it. It is a technology-oriented approach to software engineering that supports the deployment, maintenance, and evolution of large volumes of data in a cost-effective, scalable, and efficient manner. <\/p>\nData engineers design and build tools that help you guide your data through the various stages of extraction, processing, and storage. This allows you to make better decisions faster, based on data. <\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Data_Inzetten_Met_Juvo_s.jpg” title_text=”Apply_Data_With_Juvo_s” _builder_version=”4.27.5″ _module_preset=”default” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ module_id=”beginners” _builder_version=”4.27.5″ _module_preset=”default” custom_margin=”25px||||false|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” box_shadow_style=”preset3″ box_shadow_color=”rgba(0,0,0,0.1)” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Data-engineering-voor-beginners.jpg” alt=”Data-engineering-for-beginners” title_text=”Data-engineering-for-beginners” force_fullwidth=”on” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData Engineering for beginners<\/h2>\nBefore diving into the details of data engineering, it is important to be familiar with its key aspects. Are you new to data engineering? Then be sure to read these blogs first: <\/p>\n[\/et_pb_text][et_pb_blog include_categories=”42″ show_thumbnail=”off” show_author=”off” show_date=”off” show_categories=”off” show_excerpt=”off” show_pagination=”off” _builder_version=”4.17.6″ _module_preset=”default” header_level=”h3″ header_font_size=”12px” body_line_height=”0em” custom_padding=”||||false|false” global_colors_info=”{}”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”75px||75px||true|false” locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”50px||0px||false|false” custom_padding=”||0px||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat you will learn about data engineering<\/h2>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,1_3,1_3″ _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”0px||||false|false” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text ul_type=”none” ul_position=”inside” _builder_version=”4.27.5″ _module_preset=”default” link_text_color=”#000000″ global_colors_info=”{}”]<\/p>\n\n<\/i><\/span> What is Data Engineering?<\/a><\/li>\n <\/i><\/span>Data Engineering for beginners<\/a><\/li>\n<\/i><\/span> Data Engineering techniques<\/a><\/li>\n <\/i><\/span>Types of Data Engineering<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text ul_type=”none” ul_position=”inside” _builder_version=”4.27.5″ _module_preset=”default” link_text_color=”#000000″ global_colors_info=”{}”]<\/p>\n\n<\/i><\/span>Learning Data Engineering<\/a><\/li>\n<\/i><\/span>Data Engineering platforms & tools<\/a><\/li>\n<\/i><\/span>Data Engineering Programming<\/a><\/li>\n<\/i><\/span>Data Engineering news<\/a><\/li>\n<\/i><\/span>Skills of a Data Engineer<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text ul_type=”none” ul_position=”inside” _builder_version=”4.27.5″ _module_preset=”default” link_text_color=”#000000″ global_colors_info=”{}”]<\/p>\n\n<\/i><\/span>What does a Data Engineer do?<\/a><\/li>\n<\/i><\/span>The importance of Data Engineering<\/a><\/li>\n<\/i><\/span>Data Engineering process<\/a><\/li>\n<\/i><\/span>Tasks of a Data Engineer<\/a><\/li>\n<\/i><\/span>Data Engineering terminology<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” custom_padding=”100px||100px||true|false” top_divider_height=”50px” top_divider_height_tablet=”50px” top_divider_height_phone=”25px” top_divider_height_last_edited=”on|phone” bottom_divider_color=”#FFFFFF” bottom_divider_height=”50px” bottom_divider_height_tablet=”50px” bottom_divider_height_phone=”25px” bottom_divider_height_last_edited=”on|desktop” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”2_5,3_5″ module_id=”beginners” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”25px||||false|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” box_shadow_style=”preset3″ box_shadow_color=”rgba(0,0,0,0.1)” global_colors_info=”{}”][et_pb_column type=”2_5″ _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Data-engineering-technieken.jpg” alt=”Data-engineering-techniques” title_text=”Data-engineering-techniques” force_fullwidth=”on” _builder_version=”4.27.5″ _module_preset=”default” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||10px||false|false” global_colors_info=”{}”]<\/p>\nData engineering techniques<\/h2>\nIn data engineering, many different techniques are available. We will guide you through each technique. <\/p>\n[\/et_pb_text][et_pb_blog include_categories=”47″ show_thumbnail=”off” show_author=”off” show_date=”off” show_categories=”off” show_excerpt=”off” show_pagination=”off” _builder_version=”4.20.4″ _module_preset=”default” header_level=”h3″ header_font=”||||||||” header_font_size=”12px” body_line_height=”0em” custom_padding=”||||false|false” locked=”off” global_colors_info=”{}”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”75px||75px||true|false” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ module_id=”beginners” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”25px||||false|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” box_shadow_style=”preset3″ box_shadow_color=”rgba(0,0,0,0.1)” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Soorten-data-engineering.jpg” alt=”Types-of-data-engineering” title_text=”Types-of-data-engineering” force_fullwidth=”on” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nTypes of Data Engineering<\/h2>\nEvery company requires a different Data Engineering solution depending on its goals. Therefore, several types of Data Engineering exist: <\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||||false|false” global_colors_info=”{}”]<\/p>\nBig data engineering<\/a><\/h3>\nBig Data Engineering is the process of setting up, developing, and maintaining an infrastructure for processing, storing, and analyzing <\/span>large volumes of data<\/strong>, also known as “big data.”<\/span><\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||0px||false|false” global_colors_info=”{}”]<\/p>\nCloud data engineering<\/a><\/h3>\nCloud data engineering involves designing, building, and maintaining systems for the storage, processing, and analysis of data in a cloud computing environment. <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row module_id=”leren” _builder_version=”4.27.5″ _module_preset=”default” background_enable_color=”off” use_background_color_gradient=”on” background_color_gradient_direction=”90deg” background_color_gradient_stops=”rgba(0,0,0,0.5) 33%|rgba(0,0,0,0) 100%” background_color_gradient_overlays_image=”on” background_color_gradient_start=”rgba(0,0,0,0.5)” background_color_gradient_start_position=”33%” background_color_gradient_end=”rgba(0,0,0,0)” background_image=”https:\/\/juvo.be\/wp-content\/uploads\/Juvo_Data_Events_df.jpg” custom_margin=”75px||75px||true|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” link_font=”|800|||||||” link_text_color=”#FFFFFF” background_layout=”dark” global_colors_info=”{}”]<\/p>\nLearning data engineering<\/h2>\n\nJuvo regularly organizes webinars and info sessions on data engineering. We guide you through the latest developments and techniques and answer your questions.\n<\/p>\n[\/et_pb_text][et_pb_button button_url=”https:\/\/juvo.be\/en\/events\/” button_text=”Webinars” _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ module_id=”beginners” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”25px||||false|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” box_shadow_style=”preset3″ box_shadow_color=”rgba(0,0,0,0.1)” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Data-engeering-tools-platformen.jpg” alt=”Data-engineering-tools-platforms” title_text=”Data-engineering-tools-platforms” force_fullwidth=”on” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData Engineering Platforms & Tools<\/h2>\nData engineering tools are key to maximizing productivity, as they are essential for any company looking to make better business decisions by analyzing their data. There are many big data tools that can be used for various purposes, some of which are listed below: <\/p>\n[\/et_pb_text][et_pb_blog include_categories=”46″ show_thumbnail=”off” show_author=”off” show_date=”off” show_categories=”off” show_excerpt=”off” show_pagination=”off” _builder_version=”4.17.6″ _module_preset=”default” header_level=”h3″ header_font_size=”12px” body_line_height=”0em” custom_padding=”||||false|false” global_colors_info=”{}”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ specialty=”on” custom_padding_last_edited=”off|tablet” module_id=”programming” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” custom_padding=”||||true|false” custom_padding_tablet=”50px||||false|false” top_divider_height=”50px” top_divider_flip=”vertical” locked=”off” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text ul_type=”none” ul_position=”inside” _builder_version=”4.27.5″ _module_preset=”default” text_text_color=”#000000″ header_2_text_color=”#000000″ background_layout=”dark” custom_margin=”50px||0px||false|false” custom_margin_tablet=”0px||||false|false” custom_margin_phone=”” custom_margin_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\nData engineering programming<\/a><\/h2>\nVarious programming languages are used in Data Engineering. Some are more well-known and user-friendly than others. We list the tools for you and explain them in detail. <\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ specialty_columns=”2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_row_inner column_structure=”1_2,1_2″ use_custom_gutter=”on” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”50px||||false|false” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n<\/p>\nPython<\/a><\/h3>\n[\/et_pb_text][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n<\/p>\nJava<\/a><\/h3>\n[\/et_pb_text][\/et_pb_column_inner][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n<\/p>\nSQL<\/a><\/h3>\n[\/et_pb_text][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n<\/p>\nScala<\/a><\/h3>\n[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.17.6″ _module_preset=”default” locked=”off” global_colors_info=”{}”][et_pb_row module_id=”nieuws” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering news<\/h2>\n[\/et_pb_text][et_pb_blog fullwidth=”off” posts_number=”3″ include_categories=”48″ use_manual_excerpt=”off” show_author=”off” show_date=”off” show_categories=”off” show_pagination=”off” _builder_version=”4.27.5″ _module_preset=”default” header_level=”h3″ border_width_all=”0px” border_radii_image=”on|30px|30px|30px|30px” global_colors_info=”{}”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”data-engineer” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” background_enable_image=”off” custom_padding=”||||true|false” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”3_5,2_5″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”3_5″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat does a data engineer do?<\/h2>\nData engineers build, manage, and maintain applications that collect, organize, analyze, and store data. They combine computer science and business skills to analyze complex data problems and produce practical solutions that solve business challenges. <\/p>\nIt is the task of a data engineer to collect raw, unstructured datasets and master them through various machine learning techniques and algorithms. This is achieved by extracting information from the datasets to create algorithms that help companies take action based on what they have learned. <\/p>\nWith the rise of big data and analytics, all roles within the field of data engineering have become highly popular.<\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”2_5″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|25px|25px|25px|true|true” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWorking as a Data engineer<\/h2>\nLooking to build a career as a Data Engineer? At Juvo, you will find the most challenging Data Engineer jobs. <\/p>\n[\/et_pb_text][et_pb_button button_url=”https:\/\/jobs.juvo.be\/vacatures\/data-engineer\/” button_text=”Data engineering vacancy” _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”importance” _builder_version=”4.17.6″ _module_preset=”default” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ module_id=”belang” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text ul_position=”inside” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nThe importance of data engineering<\/b><\/h2>\nAs previously mentioned, data engineering helps structure the daily flow of massive amounts of data. Consequently, it enables companies to improve data for usability. Furthermore, it is crucial for the following activities: <\/span><\/p>\n\nFinding best practices to improve the software development lifecycle and assisting in their implementation.<\/span><\/li>\nImproving information security and protecting the company against online attacks.<\/span><\/li>\nIncreasing knowledge of the business domain<\/span><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Belang.Data_.Engineering.jpg” alt=”Importance.Data.Engineering” title_text=”Importance.Data.Engineering” _builder_version=”4.27.5″ _module_preset=”default” hover_enabled=”0″ border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}” force_fullwidth=”on” sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”process” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#FFFFFF” hover_enabled=”0″ locked=”off” global_colors_info=”{}” sticky_enabled=”0″][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData Engineering process<\/b><\/h2>\nWhat is it? <\/b><\/h3>\nData engineering is the conversion of raw data from various sources into a format that can be used to create meaningful products and services. It involves identifying key information, transforming data for relevance, delivering it in formats that tell a clear story, and using advanced technology to enhance that story. <\/span><\/p>\nThe data engineering process (also known as the data science or business intelligence process) collects and analyzes data for use in the organization’s decision-making process. Most importantly, the data engineering process allows companies to quickly gain meaningful insights while keeping their costs low. <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Juvo_Data_Winstgevend_Maken.jpg” alt=”Juvo – Data IT Staffing” title_text=”Juvo – Data IT Staffing” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”tasks” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” background_enable_image=”off” custom_padding=”||||false|false” locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.5″ _module_preset=”default” text_text_color=”#000000″ header_2_text_color=”#000000″ header_2_font_size=”30px” background_layout=”dark” header_2_font_size_tablet=”” header_2_font_size_phone=”28px” header_2_font_size_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\nTasks of a Data Engineer<\/b><\/h2>\nData engineers analyze and organize data, investigating patterns and discrepancies that may affect business objectives. Data engineers also use soft skills to evaluate data trends for the company and assist businesses in utilizing the collected data. Other typical data engineering tasks include: <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”||15px||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Data acquisition” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nCollecting, analyzing, and storing data.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Patterns” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding hidden patterns in data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Procedures” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nDeveloping procedures using data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Architecture” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nBuilding, generating, testing, and maintaining data architectures<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||||false|false” custom_padding=”15px||||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Preparation” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nPreparing data for prescriptive and predictive modeling<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Automate” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nUsing data to identify tasks that can be automated.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Strategy” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding strategies to improve data quality, efficiency, and reliability.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Inform” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nProviding updates to stakeholders using analytics<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”skills” _builder_version=”4.17.6″ _module_preset=”default” background_color=”#F2F2F2″ locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat skills should a Data Engineer possess?<\/strong><\/h2>\nWhile data engineers are theoretically software engineers, their capabilities go beyond what can be achieved with conventional programming skills.<\/p>\nData engineers must be familiar with these tools and skills to perform their tasks properly.<\/p>\nETL tools<\/strong>ETL<\/a> stands for extract, transform, and load. This type of tool refers to a group of data integration technologies. Low-code development platforms have largely replaced today’s traditional ETL tools. However, the ETL procedure remains crucial for data engineering in general. <\/p>\nSome of the best-known tools for this are Informatica and SAP Data Services.<\/p>\nProgramming languages used in Data Engineering<\/strong>Data engineering uses various back-end, query, and specialized languages for statistical calculations. Popular programming languages for data engineering<\/a> include Java, C#, R, Ruby, SQL, and Python. A common combination is R, Python, and SQL. <\/p>\nPython is a simple, general-purpose programming language with an extensive library. Its powerful and adaptable nature makes it ideal for ETL. ETL tasks are performed using a structured query language (SQL). <\/p>\nRelational databases play a significant role in data engineering, and SQL is the primary language for querying them. R is the premier programming language and software environment for statistical calculations and is highly favored by analysts and data miners. <\/p>\nAPIs<\/strong>Application programming interfaces (APIs) are essentially a requirement for anything related to data integration, including data engineering, of course. Every software engineering project needs APIs. They transfer data between applications and serve as a connection between those applications. <\/p>\nREST APIs are extremely important for data engineering. REST or representational state transfer APIs are excellent for any web-based tool because they can communicate over HTTP. <\/p>\nData Lakes and Data Warehouses<\/strong>Data warehouses and data lakes are massive, complex datasets that companies store for business intelligence. Business analysts process these datasets via computer clusters in business-driven information engineering. This computer network makes it easier to solve problems. <\/p>\nTwo well-known big data frameworks are Spark and Hadoop. These frameworks are used to prepare and process large datasets. They each utilize computer clusters to perform operations on massive amounts of data, such as data mining and data analysis. <\/p>\n <\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”terminology” _builder_version=”4.17.6″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Happy_customer.jpg” title_text=”Two business partners working in office” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|6px|6px|6px|6px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering terminology<\/b><\/h2>\n\nData Lake<\/a><\/li>\nData Vault<\/a><\/li>\nSnowflake<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"Data Engineering What is Data Engineering? 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Data engineering is a field that works with data to find meaning, make sense of it, and ultimately extract value from it. It is a technology-oriented approach to software engineering that supports the deployment, maintenance, and evolution of large volumes of data in a cost-effective, scalable, and efficient manner. <\/p>\n
Data engineers design and build tools that help you guide your data through the various stages of extraction, processing, and storage. This allows you to make better decisions faster, based on data. <\/p>\n
[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Data_Inzetten_Met_Juvo_s.jpg” title_text=”Apply_Data_With_Juvo_s” _builder_version=”4.27.5″ _module_preset=”default” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ module_id=”beginners” _builder_version=”4.27.5″ _module_preset=”default” custom_margin=”25px||||false|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” box_shadow_style=”preset3″ box_shadow_color=”rgba(0,0,0,0.1)” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Data-engineering-voor-beginners.jpg” alt=”Data-engineering-for-beginners” title_text=”Data-engineering-for-beginners” force_fullwidth=”on” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
Before diving into the details of data engineering, it is important to be familiar with its key aspects. Are you new to data engineering? Then be sure to read these blogs first: <\/p>\n
[\/et_pb_text][et_pb_blog include_categories=”42″ show_thumbnail=”off” show_author=”off” show_date=”off” show_categories=”off” show_excerpt=”off” show_pagination=”off” _builder_version=”4.17.6″ _module_preset=”default” header_level=”h3″ header_font_size=”12px” body_line_height=”0em” custom_padding=”||||false|false” global_colors_info=”{}”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”75px||75px||true|false” locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”50px||0px||false|false” custom_padding=”||0px||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,1_3,1_3″ _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”0px||||false|false” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text ul_type=”none” ul_position=”inside” _builder_version=”4.27.5″ _module_preset=”default” link_text_color=”#000000″ global_colors_info=”{}”]<\/p>\n
[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text ul_type=”none” ul_position=”inside” _builder_version=”4.27.5″ _module_preset=”default” link_text_color=”#000000″ global_colors_info=”{}”]<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” custom_padding=”100px||100px||true|false” top_divider_height=”50px” top_divider_height_tablet=”50px” top_divider_height_phone=”25px” top_divider_height_last_edited=”on|phone” bottom_divider_color=”#FFFFFF” bottom_divider_height=”50px” bottom_divider_height_tablet=”50px” bottom_divider_height_phone=”25px” bottom_divider_height_last_edited=”on|desktop” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”2_5,3_5″ module_id=”beginners” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”25px||||false|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” box_shadow_style=”preset3″ box_shadow_color=”rgba(0,0,0,0.1)” global_colors_info=”{}”][et_pb_column type=”2_5″ _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Data-engineering-technieken.jpg” alt=”Data-engineering-techniques” title_text=”Data-engineering-techniques” force_fullwidth=”on” _builder_version=”4.27.5″ _module_preset=”default” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||10px||false|false” global_colors_info=”{}”]<\/p>\n
In data engineering, many different techniques are available. We will guide you through each technique. <\/p>\n
[\/et_pb_text][et_pb_blog include_categories=”47″ show_thumbnail=”off” show_author=”off” show_date=”off” show_categories=”off” show_excerpt=”off” show_pagination=”off” _builder_version=”4.20.4″ _module_preset=”default” header_level=”h3″ header_font=”||||||||” header_font_size=”12px” body_line_height=”0em” custom_padding=”||||false|false” locked=”off” global_colors_info=”{}”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”75px||75px||true|false” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ module_id=”beginners” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”25px||||false|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” box_shadow_style=”preset3″ box_shadow_color=”rgba(0,0,0,0.1)” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Soorten-data-engineering.jpg” alt=”Types-of-data-engineering” title_text=”Types-of-data-engineering” force_fullwidth=”on” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
Every company requires a different Data Engineering solution depending on its goals. Therefore, several types of Data Engineering exist: <\/p>\n
[\/et_pb_text][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||||false|false” global_colors_info=”{}”]<\/p>\n
Big Data Engineering is the process of setting up, developing, and maintaining an infrastructure for processing, storing, and analyzing <\/span>large volumes of data<\/strong>, also known as “big data.”<\/span><\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||0px||false|false” global_colors_info=”{}”]<\/p>\nCloud data engineering<\/a><\/h3>\nCloud data engineering involves designing, building, and maintaining systems for the storage, processing, and analysis of data in a cloud computing environment. <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row module_id=”leren” _builder_version=”4.27.5″ _module_preset=”default” background_enable_color=”off” use_background_color_gradient=”on” background_color_gradient_direction=”90deg” background_color_gradient_stops=”rgba(0,0,0,0.5) 33%|rgba(0,0,0,0) 100%” background_color_gradient_overlays_image=”on” background_color_gradient_start=”rgba(0,0,0,0.5)” background_color_gradient_start_position=”33%” background_color_gradient_end=”rgba(0,0,0,0)” background_image=”https:\/\/juvo.be\/wp-content\/uploads\/Juvo_Data_Events_df.jpg” custom_margin=”75px||75px||true|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” link_font=”|800|||||||” link_text_color=”#FFFFFF” background_layout=”dark” global_colors_info=”{}”]<\/p>\nLearning data engineering<\/h2>\n\nJuvo regularly organizes webinars and info sessions on data engineering. We guide you through the latest developments and techniques and answer your questions.\n<\/p>\n[\/et_pb_text][et_pb_button button_url=”https:\/\/juvo.be\/en\/events\/” button_text=”Webinars” _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ module_id=”beginners” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”25px||||false|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” box_shadow_style=”preset3″ box_shadow_color=”rgba(0,0,0,0.1)” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Data-engeering-tools-platformen.jpg” alt=”Data-engineering-tools-platforms” title_text=”Data-engineering-tools-platforms” force_fullwidth=”on” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData Engineering Platforms & Tools<\/h2>\nData engineering tools are key to maximizing productivity, as they are essential for any company looking to make better business decisions by analyzing their data. There are many big data tools that can be used for various purposes, some of which are listed below: <\/p>\n[\/et_pb_text][et_pb_blog include_categories=”46″ show_thumbnail=”off” show_author=”off” show_date=”off” show_categories=”off” show_excerpt=”off” show_pagination=”off” _builder_version=”4.17.6″ _module_preset=”default” header_level=”h3″ header_font_size=”12px” body_line_height=”0em” custom_padding=”||||false|false” global_colors_info=”{}”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ specialty=”on” custom_padding_last_edited=”off|tablet” module_id=”programming” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” custom_padding=”||||true|false” custom_padding_tablet=”50px||||false|false” top_divider_height=”50px” top_divider_flip=”vertical” locked=”off” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text ul_type=”none” ul_position=”inside” _builder_version=”4.27.5″ _module_preset=”default” text_text_color=”#000000″ header_2_text_color=”#000000″ background_layout=”dark” custom_margin=”50px||0px||false|false” custom_margin_tablet=”0px||||false|false” custom_margin_phone=”” custom_margin_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\nData engineering programming<\/a><\/h2>\nVarious programming languages are used in Data Engineering. Some are more well-known and user-friendly than others. We list the tools for you and explain them in detail. <\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ specialty_columns=”2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_row_inner column_structure=”1_2,1_2″ use_custom_gutter=”on” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”50px||||false|false” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n<\/p>\nPython<\/a><\/h3>\n[\/et_pb_text][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n<\/p>\nJava<\/a><\/h3>\n[\/et_pb_text][\/et_pb_column_inner][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n<\/p>\nSQL<\/a><\/h3>\n[\/et_pb_text][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n<\/p>\nScala<\/a><\/h3>\n[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.17.6″ _module_preset=”default” locked=”off” global_colors_info=”{}”][et_pb_row module_id=”nieuws” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering news<\/h2>\n[\/et_pb_text][et_pb_blog fullwidth=”off” posts_number=”3″ include_categories=”48″ use_manual_excerpt=”off” show_author=”off” show_date=”off” show_categories=”off” show_pagination=”off” _builder_version=”4.27.5″ _module_preset=”default” header_level=”h3″ border_width_all=”0px” border_radii_image=”on|30px|30px|30px|30px” global_colors_info=”{}”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”data-engineer” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” background_enable_image=”off” custom_padding=”||||true|false” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”3_5,2_5″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”3_5″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat does a data engineer do?<\/h2>\nData engineers build, manage, and maintain applications that collect, organize, analyze, and store data. They combine computer science and business skills to analyze complex data problems and produce practical solutions that solve business challenges. <\/p>\nIt is the task of a data engineer to collect raw, unstructured datasets and master them through various machine learning techniques and algorithms. This is achieved by extracting information from the datasets to create algorithms that help companies take action based on what they have learned. <\/p>\nWith the rise of big data and analytics, all roles within the field of data engineering have become highly popular.<\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”2_5″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|25px|25px|25px|true|true” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWorking as a Data engineer<\/h2>\nLooking to build a career as a Data Engineer? At Juvo, you will find the most challenging Data Engineer jobs. <\/p>\n[\/et_pb_text][et_pb_button button_url=”https:\/\/jobs.juvo.be\/vacatures\/data-engineer\/” button_text=”Data engineering vacancy” _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”importance” _builder_version=”4.17.6″ _module_preset=”default” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ module_id=”belang” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text ul_position=”inside” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nThe importance of data engineering<\/b><\/h2>\nAs previously mentioned, data engineering helps structure the daily flow of massive amounts of data. Consequently, it enables companies to improve data for usability. Furthermore, it is crucial for the following activities: <\/span><\/p>\n\nFinding best practices to improve the software development lifecycle and assisting in their implementation.<\/span><\/li>\nImproving information security and protecting the company against online attacks.<\/span><\/li>\nIncreasing knowledge of the business domain<\/span><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Belang.Data_.Engineering.jpg” alt=”Importance.Data.Engineering” title_text=”Importance.Data.Engineering” _builder_version=”4.27.5″ _module_preset=”default” hover_enabled=”0″ border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}” force_fullwidth=”on” sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”process” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#FFFFFF” hover_enabled=”0″ locked=”off” global_colors_info=”{}” sticky_enabled=”0″][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData Engineering process<\/b><\/h2>\nWhat is it? <\/b><\/h3>\nData engineering is the conversion of raw data from various sources into a format that can be used to create meaningful products and services. It involves identifying key information, transforming data for relevance, delivering it in formats that tell a clear story, and using advanced technology to enhance that story. <\/span><\/p>\nThe data engineering process (also known as the data science or business intelligence process) collects and analyzes data for use in the organization’s decision-making process. Most importantly, the data engineering process allows companies to quickly gain meaningful insights while keeping their costs low. <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Juvo_Data_Winstgevend_Maken.jpg” alt=”Juvo – Data IT Staffing” title_text=”Juvo – Data IT Staffing” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”tasks” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” background_enable_image=”off” custom_padding=”||||false|false” locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.5″ _module_preset=”default” text_text_color=”#000000″ header_2_text_color=”#000000″ header_2_font_size=”30px” background_layout=”dark” header_2_font_size_tablet=”” header_2_font_size_phone=”28px” header_2_font_size_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\nTasks of a Data Engineer<\/b><\/h2>\nData engineers analyze and organize data, investigating patterns and discrepancies that may affect business objectives. Data engineers also use soft skills to evaluate data trends for the company and assist businesses in utilizing the collected data. Other typical data engineering tasks include: <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”||15px||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Data acquisition” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nCollecting, analyzing, and storing data.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Patterns” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding hidden patterns in data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Procedures” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nDeveloping procedures using data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Architecture” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nBuilding, generating, testing, and maintaining data architectures<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||||false|false” custom_padding=”15px||||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Preparation” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nPreparing data for prescriptive and predictive modeling<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Automate” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nUsing data to identify tasks that can be automated.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Strategy” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding strategies to improve data quality, efficiency, and reliability.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Inform” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nProviding updates to stakeholders using analytics<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”skills” _builder_version=”4.17.6″ _module_preset=”default” background_color=”#F2F2F2″ locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat skills should a Data Engineer possess?<\/strong><\/h2>\nWhile data engineers are theoretically software engineers, their capabilities go beyond what can be achieved with conventional programming skills.<\/p>\nData engineers must be familiar with these tools and skills to perform their tasks properly.<\/p>\nETL tools<\/strong>ETL<\/a> stands for extract, transform, and load. This type of tool refers to a group of data integration technologies. Low-code development platforms have largely replaced today’s traditional ETL tools. However, the ETL procedure remains crucial for data engineering in general. <\/p>\nSome of the best-known tools for this are Informatica and SAP Data Services.<\/p>\nProgramming languages used in Data Engineering<\/strong>Data engineering uses various back-end, query, and specialized languages for statistical calculations. Popular programming languages for data engineering<\/a> include Java, C#, R, Ruby, SQL, and Python. A common combination is R, Python, and SQL. <\/p>\nPython is a simple, general-purpose programming language with an extensive library. Its powerful and adaptable nature makes it ideal for ETL. ETL tasks are performed using a structured query language (SQL). <\/p>\nRelational databases play a significant role in data engineering, and SQL is the primary language for querying them. R is the premier programming language and software environment for statistical calculations and is highly favored by analysts and data miners. <\/p>\nAPIs<\/strong>Application programming interfaces (APIs) are essentially a requirement for anything related to data integration, including data engineering, of course. Every software engineering project needs APIs. They transfer data between applications and serve as a connection between those applications. <\/p>\nREST APIs are extremely important for data engineering. REST or representational state transfer APIs are excellent for any web-based tool because they can communicate over HTTP. <\/p>\nData Lakes and Data Warehouses<\/strong>Data warehouses and data lakes are massive, complex datasets that companies store for business intelligence. Business analysts process these datasets via computer clusters in business-driven information engineering. This computer network makes it easier to solve problems. <\/p>\nTwo well-known big data frameworks are Spark and Hadoop. These frameworks are used to prepare and process large datasets. They each utilize computer clusters to perform operations on massive amounts of data, such as data mining and data analysis. <\/p>\n <\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”terminology” _builder_version=”4.17.6″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Happy_customer.jpg” title_text=”Two business partners working in office” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|6px|6px|6px|6px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering terminology<\/b><\/h2>\n\nData Lake<\/a><\/li>\nData Vault<\/a><\/li>\nSnowflake<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"Data Engineering What is Data Engineering? 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Cloud data engineering involves designing, building, and maintaining systems for the storage, processing, and analysis of data in a cloud computing environment. <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row module_id=”leren” _builder_version=”4.27.5″ _module_preset=”default” background_enable_color=”off” use_background_color_gradient=”on” background_color_gradient_direction=”90deg” background_color_gradient_stops=”rgba(0,0,0,0.5) 33%|rgba(0,0,0,0) 100%” background_color_gradient_overlays_image=”on” background_color_gradient_start=”rgba(0,0,0,0.5)” background_color_gradient_start_position=”33%” background_color_gradient_end=”rgba(0,0,0,0)” background_image=”https:\/\/juvo.be\/wp-content\/uploads\/Juvo_Data_Events_df.jpg” custom_margin=”75px||75px||true|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” link_font=”|800|||||||” link_text_color=”#FFFFFF” background_layout=”dark” global_colors_info=”{}”]<\/p>\nLearning data engineering<\/h2>\n\nJuvo regularly organizes webinars and info sessions on data engineering. We guide you through the latest developments and techniques and answer your questions.\n<\/p>\n[\/et_pb_text][et_pb_button button_url=”https:\/\/juvo.be\/en\/events\/” button_text=”Webinars” _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ module_id=”beginners” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”25px||||false|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” box_shadow_style=”preset3″ box_shadow_color=”rgba(0,0,0,0.1)” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Data-engeering-tools-platformen.jpg” alt=”Data-engineering-tools-platforms” title_text=”Data-engineering-tools-platforms” force_fullwidth=”on” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData Engineering Platforms & Tools<\/h2>\nData engineering tools are key to maximizing productivity, as they are essential for any company looking to make better business decisions by analyzing their data. There are many big data tools that can be used for various purposes, some of which are listed below: <\/p>\n[\/et_pb_text][et_pb_blog include_categories=”46″ show_thumbnail=”off” show_author=”off” show_date=”off” show_categories=”off” show_excerpt=”off” show_pagination=”off” _builder_version=”4.17.6″ _module_preset=”default” header_level=”h3″ header_font_size=”12px” body_line_height=”0em” custom_padding=”||||false|false” global_colors_info=”{}”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ specialty=”on” custom_padding_last_edited=”off|tablet” module_id=”programming” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” custom_padding=”||||true|false” custom_padding_tablet=”50px||||false|false” top_divider_height=”50px” top_divider_flip=”vertical” locked=”off” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text ul_type=”none” ul_position=”inside” _builder_version=”4.27.5″ _module_preset=”default” text_text_color=”#000000″ header_2_text_color=”#000000″ background_layout=”dark” custom_margin=”50px||0px||false|false” custom_margin_tablet=”0px||||false|false” custom_margin_phone=”” custom_margin_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\nData engineering programming<\/a><\/h2>\nVarious programming languages are used in Data Engineering. Some are more well-known and user-friendly than others. We list the tools for you and explain them in detail. <\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ specialty_columns=”2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_row_inner column_structure=”1_2,1_2″ use_custom_gutter=”on” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”50px||||false|false” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n<\/p>\nPython<\/a><\/h3>\n[\/et_pb_text][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n<\/p>\nJava<\/a><\/h3>\n[\/et_pb_text][\/et_pb_column_inner][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n<\/p>\nSQL<\/a><\/h3>\n[\/et_pb_text][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n<\/p>\nScala<\/a><\/h3>\n[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.17.6″ _module_preset=”default” locked=”off” global_colors_info=”{}”][et_pb_row module_id=”nieuws” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering news<\/h2>\n[\/et_pb_text][et_pb_blog fullwidth=”off” posts_number=”3″ include_categories=”48″ use_manual_excerpt=”off” show_author=”off” show_date=”off” show_categories=”off” show_pagination=”off” _builder_version=”4.27.5″ _module_preset=”default” header_level=”h3″ border_width_all=”0px” border_radii_image=”on|30px|30px|30px|30px” global_colors_info=”{}”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”data-engineer” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” background_enable_image=”off” custom_padding=”||||true|false” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”3_5,2_5″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”3_5″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat does a data engineer do?<\/h2>\nData engineers build, manage, and maintain applications that collect, organize, analyze, and store data. They combine computer science and business skills to analyze complex data problems and produce practical solutions that solve business challenges. <\/p>\nIt is the task of a data engineer to collect raw, unstructured datasets and master them through various machine learning techniques and algorithms. This is achieved by extracting information from the datasets to create algorithms that help companies take action based on what they have learned. <\/p>\nWith the rise of big data and analytics, all roles within the field of data engineering have become highly popular.<\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”2_5″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|25px|25px|25px|true|true” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWorking as a Data engineer<\/h2>\nLooking to build a career as a Data Engineer? At Juvo, you will find the most challenging Data Engineer jobs. <\/p>\n[\/et_pb_text][et_pb_button button_url=”https:\/\/jobs.juvo.be\/vacatures\/data-engineer\/” button_text=”Data engineering vacancy” _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”importance” _builder_version=”4.17.6″ _module_preset=”default” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ module_id=”belang” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text ul_position=”inside” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nThe importance of data engineering<\/b><\/h2>\nAs previously mentioned, data engineering helps structure the daily flow of massive amounts of data. Consequently, it enables companies to improve data for usability. Furthermore, it is crucial for the following activities: <\/span><\/p>\n\nFinding best practices to improve the software development lifecycle and assisting in their implementation.<\/span><\/li>\nImproving information security and protecting the company against online attacks.<\/span><\/li>\nIncreasing knowledge of the business domain<\/span><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Belang.Data_.Engineering.jpg” alt=”Importance.Data.Engineering” title_text=”Importance.Data.Engineering” _builder_version=”4.27.5″ _module_preset=”default” hover_enabled=”0″ border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}” force_fullwidth=”on” sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”process” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#FFFFFF” hover_enabled=”0″ locked=”off” global_colors_info=”{}” sticky_enabled=”0″][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData Engineering process<\/b><\/h2>\nWhat is it? <\/b><\/h3>\nData engineering is the conversion of raw data from various sources into a format that can be used to create meaningful products and services. It involves identifying key information, transforming data for relevance, delivering it in formats that tell a clear story, and using advanced technology to enhance that story. <\/span><\/p>\nThe data engineering process (also known as the data science or business intelligence process) collects and analyzes data for use in the organization’s decision-making process. Most importantly, the data engineering process allows companies to quickly gain meaningful insights while keeping their costs low. <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Juvo_Data_Winstgevend_Maken.jpg” alt=”Juvo – Data IT Staffing” title_text=”Juvo – Data IT Staffing” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”tasks” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” background_enable_image=”off” custom_padding=”||||false|false” locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.5″ _module_preset=”default” text_text_color=”#000000″ header_2_text_color=”#000000″ header_2_font_size=”30px” background_layout=”dark” header_2_font_size_tablet=”” header_2_font_size_phone=”28px” header_2_font_size_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\nTasks of a Data Engineer<\/b><\/h2>\nData engineers analyze and organize data, investigating patterns and discrepancies that may affect business objectives. Data engineers also use soft skills to evaluate data trends for the company and assist businesses in utilizing the collected data. Other typical data engineering tasks include: <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”||15px||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Data acquisition” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nCollecting, analyzing, and storing data.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Patterns” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding hidden patterns in data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Procedures” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nDeveloping procedures using data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Architecture” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nBuilding, generating, testing, and maintaining data architectures<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||||false|false” custom_padding=”15px||||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Preparation” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nPreparing data for prescriptive and predictive modeling<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Automate” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nUsing data to identify tasks that can be automated.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Strategy” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding strategies to improve data quality, efficiency, and reliability.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Inform” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nProviding updates to stakeholders using analytics<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”skills” _builder_version=”4.17.6″ _module_preset=”default” background_color=”#F2F2F2″ locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat skills should a Data Engineer possess?<\/strong><\/h2>\nWhile data engineers are theoretically software engineers, their capabilities go beyond what can be achieved with conventional programming skills.<\/p>\nData engineers must be familiar with these tools and skills to perform their tasks properly.<\/p>\nETL tools<\/strong>ETL<\/a> stands for extract, transform, and load. This type of tool refers to a group of data integration technologies. Low-code development platforms have largely replaced today’s traditional ETL tools. However, the ETL procedure remains crucial for data engineering in general. <\/p>\nSome of the best-known tools for this are Informatica and SAP Data Services.<\/p>\nProgramming languages used in Data Engineering<\/strong>Data engineering uses various back-end, query, and specialized languages for statistical calculations. Popular programming languages for data engineering<\/a> include Java, C#, R, Ruby, SQL, and Python. A common combination is R, Python, and SQL. <\/p>\nPython is a simple, general-purpose programming language with an extensive library. Its powerful and adaptable nature makes it ideal for ETL. ETL tasks are performed using a structured query language (SQL). <\/p>\nRelational databases play a significant role in data engineering, and SQL is the primary language for querying them. R is the premier programming language and software environment for statistical calculations and is highly favored by analysts and data miners. <\/p>\nAPIs<\/strong>Application programming interfaces (APIs) are essentially a requirement for anything related to data integration, including data engineering, of course. Every software engineering project needs APIs. They transfer data between applications and serve as a connection between those applications. <\/p>\nREST APIs are extremely important for data engineering. REST or representational state transfer APIs are excellent for any web-based tool because they can communicate over HTTP. <\/p>\nData Lakes and Data Warehouses<\/strong>Data warehouses and data lakes are massive, complex datasets that companies store for business intelligence. Business analysts process these datasets via computer clusters in business-driven information engineering. This computer network makes it easier to solve problems. <\/p>\nTwo well-known big data frameworks are Spark and Hadoop. These frameworks are used to prepare and process large datasets. They each utilize computer clusters to perform operations on massive amounts of data, such as data mining and data analysis. <\/p>\n <\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”terminology” _builder_version=”4.17.6″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Happy_customer.jpg” title_text=”Two business partners working in office” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|6px|6px|6px|6px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering terminology<\/b><\/h2>\n\nData Lake<\/a><\/li>\nData Vault<\/a><\/li>\nSnowflake<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"Data Engineering What is Data Engineering? Juvo<\/span><\/li><\/ol><\/nav>Data engineering is a field that works with data to find meaning, make sense of it, and ultimately extract value from it. It is a technology-oriented approach to software engineering that supports the deployment, maintenance, and evolution of large volumes of data in a cost-effective, scalable, and efficient manner. […]<\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_seopress_titles_title":"What is Data Engineering? 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[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row module_id=”leren” _builder_version=”4.27.5″ _module_preset=”default” background_enable_color=”off” use_background_color_gradient=”on” background_color_gradient_direction=”90deg” background_color_gradient_stops=”rgba(0,0,0,0.5) 33%|rgba(0,0,0,0) 100%” background_color_gradient_overlays_image=”on” background_color_gradient_start=”rgba(0,0,0,0.5)” background_color_gradient_start_position=”33%” background_color_gradient_end=”rgba(0,0,0,0)” background_image=”https:\/\/juvo.be\/wp-content\/uploads\/Juvo_Data_Events_df.jpg” custom_margin=”75px||75px||true|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” link_font=”|800|||||||” link_text_color=”#FFFFFF” background_layout=”dark” global_colors_info=”{}”]<\/p>\n
\nJuvo regularly organizes webinars and info sessions on data engineering. We guide you through the latest developments and techniques and answer your questions.\n<\/p>\n
[\/et_pb_text][et_pb_button button_url=”https:\/\/juvo.be\/en\/events\/” button_text=”Webinars” _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ module_id=”beginners” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”25px||||false|false” custom_padding=”40px|40px|40px|40px|true|true” border_radii=”on|30px|30px|30px|30px” box_shadow_style=”preset3″ box_shadow_color=”rgba(0,0,0,0.1)” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Data-engeering-tools-platformen.jpg” alt=”Data-engineering-tools-platforms” title_text=”Data-engineering-tools-platforms” force_fullwidth=”on” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
Data engineering tools are key to maximizing productivity, as they are essential for any company looking to make better business decisions by analyzing their data. There are many big data tools that can be used for various purposes, some of which are listed below: <\/p>\n
[\/et_pb_text][et_pb_blog include_categories=”46″ show_thumbnail=”off” show_author=”off” show_date=”off” show_categories=”off” show_excerpt=”off” show_pagination=”off” _builder_version=”4.17.6″ _module_preset=”default” header_level=”h3″ header_font_size=”12px” body_line_height=”0em” custom_padding=”||||false|false” global_colors_info=”{}”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ specialty=”on” custom_padding_last_edited=”off|tablet” module_id=”programming” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” custom_padding=”||||true|false” custom_padding_tablet=”50px||||false|false” top_divider_height=”50px” top_divider_flip=”vertical” locked=”off” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text ul_type=”none” ul_position=”inside” _builder_version=”4.27.5″ _module_preset=”default” text_text_color=”#000000″ header_2_text_color=”#000000″ background_layout=”dark” custom_margin=”50px||0px||false|false” custom_margin_tablet=”0px||||false|false” custom_margin_phone=”” custom_margin_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\n
Various programming languages are used in Data Engineering. Some are more well-known and user-friendly than others. We list the tools for you and explain them in detail. <\/p>\n
[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ specialty_columns=”2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_row_inner column_structure=”1_2,1_2″ use_custom_gutter=”on” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”50px||||false|false” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n
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[\/et_pb_text][\/et_pb_column_inner][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|20px|25px|20px|true|true” animation_style=”slide” animation_direction=”bottom” animation_duration=”350ms” animation_delay=”100ms” animation_intensity_slide=”10%” animation_speed_curve=”ease-out” border_radii=”on|3px|3px|3px|3px” border_width_all=”1px” border_color_all=”#e7e7e7″ box_shadow_style=”preset3″ box_shadow_vertical=”10px” box_shadow_blur=”35px” box_shadow_spread=”0px” box_shadow_color=”rgba(38,41,77,0.1)” global_colors_info=”{}” custom_margin__hover_enabled=”off|hover” custom_margin__hover=”-10px||||false|false”]<\/p>\n
[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.17.6″ _module_preset=”default” locked=”off” global_colors_info=”{}”][et_pb_row module_id=”nieuws” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
[\/et_pb_text][et_pb_blog fullwidth=”off” posts_number=”3″ include_categories=”48″ use_manual_excerpt=”off” show_author=”off” show_date=”off” show_categories=”off” show_pagination=”off” _builder_version=”4.27.5″ _module_preset=”default” header_level=”h3″ border_width_all=”0px” border_radii_image=”on|30px|30px|30px|30px” global_colors_info=”{}”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”data-engineer” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” background_enable_image=”off” custom_padding=”||||true|false” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”3_5,2_5″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”3_5″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
Data engineers build, manage, and maintain applications that collect, organize, analyze, and store data. They combine computer science and business skills to analyze complex data problems and produce practical solutions that solve business challenges. <\/p>\n
It is the task of a data engineer to collect raw, unstructured datasets and master them through various machine learning techniques and algorithms. This is achieved by extracting information from the datasets to create algorithms that help companies take action based on what they have learned. <\/p>\n
With the rise of big data and analytics, all roles within the field of data engineering have become highly popular.<\/p>\n
[\/et_pb_text][\/et_pb_column][et_pb_column type=”2_5″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”25px|25px|25px|25px|true|true” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
Looking to build a career as a Data Engineer? At Juvo, you will find the most challenging Data Engineer jobs. <\/p>\n
[\/et_pb_text][et_pb_button button_url=”https:\/\/jobs.juvo.be\/vacatures\/data-engineer\/” button_text=”Data engineering vacancy” _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”importance” _builder_version=”4.17.6″ _module_preset=”default” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ module_id=”belang” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text ul_position=”inside” _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
As previously mentioned, data engineering helps structure the daily flow of massive amounts of data. Consequently, it enables companies to improve data for usability. Furthermore, it is crucial for the following activities: <\/span><\/p>\n\nFinding best practices to improve the software development lifecycle and assisting in their implementation.<\/span><\/li>\nImproving information security and protecting the company against online attacks.<\/span><\/li>\nIncreasing knowledge of the business domain<\/span><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Belang.Data_.Engineering.jpg” alt=”Importance.Data.Engineering” title_text=”Importance.Data.Engineering” _builder_version=”4.27.5″ _module_preset=”default” hover_enabled=”0″ border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}” force_fullwidth=”on” sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”process” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#FFFFFF” hover_enabled=”0″ locked=”off” global_colors_info=”{}” sticky_enabled=”0″][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData Engineering process<\/b><\/h2>\nWhat is it? <\/b><\/h3>\nData engineering is the conversion of raw data from various sources into a format that can be used to create meaningful products and services. It involves identifying key information, transforming data for relevance, delivering it in formats that tell a clear story, and using advanced technology to enhance that story. <\/span><\/p>\nThe data engineering process (also known as the data science or business intelligence process) collects and analyzes data for use in the organization’s decision-making process. Most importantly, the data engineering process allows companies to quickly gain meaningful insights while keeping their costs low. <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Juvo_Data_Winstgevend_Maken.jpg” alt=”Juvo – Data IT Staffing” title_text=”Juvo – Data IT Staffing” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”tasks” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” background_enable_image=”off” custom_padding=”||||false|false” locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.5″ _module_preset=”default” text_text_color=”#000000″ header_2_text_color=”#000000″ header_2_font_size=”30px” background_layout=”dark” header_2_font_size_tablet=”” header_2_font_size_phone=”28px” header_2_font_size_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\nTasks of a Data Engineer<\/b><\/h2>\nData engineers analyze and organize data, investigating patterns and discrepancies that may affect business objectives. Data engineers also use soft skills to evaluate data trends for the company and assist businesses in utilizing the collected data. Other typical data engineering tasks include: <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”||15px||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Data acquisition” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nCollecting, analyzing, and storing data.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Patterns” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding hidden patterns in data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Procedures” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nDeveloping procedures using data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Architecture” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nBuilding, generating, testing, and maintaining data architectures<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||||false|false” custom_padding=”15px||||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Preparation” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nPreparing data for prescriptive and predictive modeling<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Automate” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nUsing data to identify tasks that can be automated.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Strategy” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding strategies to improve data quality, efficiency, and reliability.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Inform” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nProviding updates to stakeholders using analytics<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”skills” _builder_version=”4.17.6″ _module_preset=”default” background_color=”#F2F2F2″ locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat skills should a Data Engineer possess?<\/strong><\/h2>\nWhile data engineers are theoretically software engineers, their capabilities go beyond what can be achieved with conventional programming skills.<\/p>\nData engineers must be familiar with these tools and skills to perform their tasks properly.<\/p>\nETL tools<\/strong>ETL<\/a> stands for extract, transform, and load. This type of tool refers to a group of data integration technologies. Low-code development platforms have largely replaced today’s traditional ETL tools. However, the ETL procedure remains crucial for data engineering in general. <\/p>\nSome of the best-known tools for this are Informatica and SAP Data Services.<\/p>\nProgramming languages used in Data Engineering<\/strong>Data engineering uses various back-end, query, and specialized languages for statistical calculations. Popular programming languages for data engineering<\/a> include Java, C#, R, Ruby, SQL, and Python. A common combination is R, Python, and SQL. <\/p>\nPython is a simple, general-purpose programming language with an extensive library. Its powerful and adaptable nature makes it ideal for ETL. ETL tasks are performed using a structured query language (SQL). <\/p>\nRelational databases play a significant role in data engineering, and SQL is the primary language for querying them. R is the premier programming language and software environment for statistical calculations and is highly favored by analysts and data miners. <\/p>\nAPIs<\/strong>Application programming interfaces (APIs) are essentially a requirement for anything related to data integration, including data engineering, of course. Every software engineering project needs APIs. They transfer data between applications and serve as a connection between those applications. <\/p>\nREST APIs are extremely important for data engineering. REST or representational state transfer APIs are excellent for any web-based tool because they can communicate over HTTP. <\/p>\nData Lakes and Data Warehouses<\/strong>Data warehouses and data lakes are massive, complex datasets that companies store for business intelligence. Business analysts process these datasets via computer clusters in business-driven information engineering. This computer network makes it easier to solve problems. <\/p>\nTwo well-known big data frameworks are Spark and Hadoop. These frameworks are used to prepare and process large datasets. They each utilize computer clusters to perform operations on massive amounts of data, such as data mining and data analysis. <\/p>\n <\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”terminology” _builder_version=”4.17.6″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Happy_customer.jpg” title_text=”Two business partners working in office” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|6px|6px|6px|6px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering terminology<\/b><\/h2>\n\nData Lake<\/a><\/li>\nData Vault<\/a><\/li>\nSnowflake<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"Data Engineering What is Data Engineering? 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[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Belang.Data_.Engineering.jpg” alt=”Importance.Data.Engineering” title_text=”Importance.Data.Engineering” _builder_version=”4.27.5″ _module_preset=”default” hover_enabled=”0″ border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}” force_fullwidth=”on” sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”process” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#FFFFFF” hover_enabled=”0″ locked=”off” global_colors_info=”{}” sticky_enabled=”0″][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
Data engineering is the conversion of raw data from various sources into a format that can be used to create meaningful products and services. It involves identifying key information, transforming data for relevance, delivering it in formats that tell a clear story, and using advanced technology to enhance that story. <\/span><\/p>\nThe data engineering process (also known as the data science or business intelligence process) collects and analyzes data for use in the organization’s decision-making process. Most importantly, the data engineering process allows companies to quickly gain meaningful insights while keeping their costs low. <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Juvo_Data_Winstgevend_Maken.jpg” alt=”Juvo – Data IT Staffing” title_text=”Juvo – Data IT Staffing” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”tasks” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” background_enable_image=”off” custom_padding=”||||false|false” locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.5″ _module_preset=”default” text_text_color=”#000000″ header_2_text_color=”#000000″ header_2_font_size=”30px” background_layout=”dark” header_2_font_size_tablet=”” header_2_font_size_phone=”28px” header_2_font_size_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\nTasks of a Data Engineer<\/b><\/h2>\nData engineers analyze and organize data, investigating patterns and discrepancies that may affect business objectives. Data engineers also use soft skills to evaluate data trends for the company and assist businesses in utilizing the collected data. Other typical data engineering tasks include: <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”||15px||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Data acquisition” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nCollecting, analyzing, and storing data.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Patterns” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding hidden patterns in data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Procedures” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nDeveloping procedures using data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Architecture” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nBuilding, generating, testing, and maintaining data architectures<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||||false|false” custom_padding=”15px||||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Preparation” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nPreparing data for prescriptive and predictive modeling<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Automate” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nUsing data to identify tasks that can be automated.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Strategy” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding strategies to improve data quality, efficiency, and reliability.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Inform” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nProviding updates to stakeholders using analytics<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”skills” _builder_version=”4.17.6″ _module_preset=”default” background_color=”#F2F2F2″ locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat skills should a Data Engineer possess?<\/strong><\/h2>\nWhile data engineers are theoretically software engineers, their capabilities go beyond what can be achieved with conventional programming skills.<\/p>\nData engineers must be familiar with these tools and skills to perform their tasks properly.<\/p>\nETL tools<\/strong>ETL<\/a> stands for extract, transform, and load. This type of tool refers to a group of data integration technologies. Low-code development platforms have largely replaced today’s traditional ETL tools. However, the ETL procedure remains crucial for data engineering in general. <\/p>\nSome of the best-known tools for this are Informatica and SAP Data Services.<\/p>\nProgramming languages used in Data Engineering<\/strong>Data engineering uses various back-end, query, and specialized languages for statistical calculations. Popular programming languages for data engineering<\/a> include Java, C#, R, Ruby, SQL, and Python. A common combination is R, Python, and SQL. <\/p>\nPython is a simple, general-purpose programming language with an extensive library. Its powerful and adaptable nature makes it ideal for ETL. ETL tasks are performed using a structured query language (SQL). <\/p>\nRelational databases play a significant role in data engineering, and SQL is the primary language for querying them. R is the premier programming language and software environment for statistical calculations and is highly favored by analysts and data miners. <\/p>\nAPIs<\/strong>Application programming interfaces (APIs) are essentially a requirement for anything related to data integration, including data engineering, of course. Every software engineering project needs APIs. They transfer data between applications and serve as a connection between those applications. <\/p>\nREST APIs are extremely important for data engineering. REST or representational state transfer APIs are excellent for any web-based tool because they can communicate over HTTP. <\/p>\nData Lakes and Data Warehouses<\/strong>Data warehouses and data lakes are massive, complex datasets that companies store for business intelligence. Business analysts process these datasets via computer clusters in business-driven information engineering. This computer network makes it easier to solve problems. <\/p>\nTwo well-known big data frameworks are Spark and Hadoop. These frameworks are used to prepare and process large datasets. They each utilize computer clusters to perform operations on massive amounts of data, such as data mining and data analysis. <\/p>\n <\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”terminology” _builder_version=”4.17.6″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Happy_customer.jpg” title_text=”Two business partners working in office” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|6px|6px|6px|6px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering terminology<\/b><\/h2>\n\nData Lake<\/a><\/li>\nData Vault<\/a><\/li>\nSnowflake<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"Data Engineering What is Data Engineering? Juvo<\/span><\/li><\/ol><\/nav>Data engineering is a field that works with data to find meaning, make sense of it, and ultimately extract value from it. It is a technology-oriented approach to software engineering that supports the deployment, maintenance, and evolution of large volumes of data in a cost-effective, scalable, and efficient manner. […]<\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_seopress_titles_title":"What is Data Engineering? 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The data engineering process (also known as the data science or business intelligence process) collects and analyzes data for use in the organization’s decision-making process. Most importantly, the data engineering process allows companies to quickly gain meaningful insights while keeping their costs low. <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Juvo_Data_Winstgevend_Maken.jpg” alt=”Juvo – Data IT Staffing” title_text=”Juvo – Data IT Staffing” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”tasks” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” background_enable_image=”off” custom_padding=”||||false|false” locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.5″ _module_preset=”default” text_text_color=”#000000″ header_2_text_color=”#000000″ header_2_font_size=”30px” background_layout=”dark” header_2_font_size_tablet=”” header_2_font_size_phone=”28px” header_2_font_size_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\nTasks of a Data Engineer<\/b><\/h2>\nData engineers analyze and organize data, investigating patterns and discrepancies that may affect business objectives. Data engineers also use soft skills to evaluate data trends for the company and assist businesses in utilizing the collected data. Other typical data engineering tasks include: <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”||15px||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Data acquisition” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nCollecting, analyzing, and storing data.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Patterns” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding hidden patterns in data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Procedures” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nDeveloping procedures using data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Architecture” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nBuilding, generating, testing, and maintaining data architectures<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||||false|false” custom_padding=”15px||||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Preparation” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nPreparing data for prescriptive and predictive modeling<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Automate” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nUsing data to identify tasks that can be automated.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Strategy” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding strategies to improve data quality, efficiency, and reliability.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Inform” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nProviding updates to stakeholders using analytics<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”skills” _builder_version=”4.17.6″ _module_preset=”default” background_color=”#F2F2F2″ locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat skills should a Data Engineer possess?<\/strong><\/h2>\nWhile data engineers are theoretically software engineers, their capabilities go beyond what can be achieved with conventional programming skills.<\/p>\nData engineers must be familiar with these tools and skills to perform their tasks properly.<\/p>\nETL tools<\/strong>ETL<\/a> stands for extract, transform, and load. This type of tool refers to a group of data integration technologies. Low-code development platforms have largely replaced today’s traditional ETL tools. However, the ETL procedure remains crucial for data engineering in general. <\/p>\nSome of the best-known tools for this are Informatica and SAP Data Services.<\/p>\nProgramming languages used in Data Engineering<\/strong>Data engineering uses various back-end, query, and specialized languages for statistical calculations. Popular programming languages for data engineering<\/a> include Java, C#, R, Ruby, SQL, and Python. A common combination is R, Python, and SQL. <\/p>\nPython is a simple, general-purpose programming language with an extensive library. Its powerful and adaptable nature makes it ideal for ETL. ETL tasks are performed using a structured query language (SQL). <\/p>\nRelational databases play a significant role in data engineering, and SQL is the primary language for querying them. R is the premier programming language and software environment for statistical calculations and is highly favored by analysts and data miners. <\/p>\nAPIs<\/strong>Application programming interfaces (APIs) are essentially a requirement for anything related to data integration, including data engineering, of course. Every software engineering project needs APIs. They transfer data between applications and serve as a connection between those applications. <\/p>\nREST APIs are extremely important for data engineering. REST or representational state transfer APIs are excellent for any web-based tool because they can communicate over HTTP. <\/p>\nData Lakes and Data Warehouses<\/strong>Data warehouses and data lakes are massive, complex datasets that companies store for business intelligence. Business analysts process these datasets via computer clusters in business-driven information engineering. This computer network makes it easier to solve problems. <\/p>\nTwo well-known big data frameworks are Spark and Hadoop. These frameworks are used to prepare and process large datasets. They each utilize computer clusters to perform operations on massive amounts of data, such as data mining and data analysis. <\/p>\n <\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”terminology” _builder_version=”4.17.6″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Happy_customer.jpg” title_text=”Two business partners working in office” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|6px|6px|6px|6px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering terminology<\/b><\/h2>\n\nData Lake<\/a><\/li>\nData Vault<\/a><\/li>\nSnowflake<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"Data Engineering What is Data Engineering? 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[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Juvo_Data_Winstgevend_Maken.jpg” alt=”Juvo – Data IT Staffing” title_text=”Juvo – Data IT Staffing” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”tasks” _builder_version=”4.27.5″ _module_preset=”default” background_color=”#EDEDED” background_enable_image=”off” custom_padding=”||||false|false” locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.5″ _module_preset=”default” text_text_color=”#000000″ header_2_text_color=”#000000″ header_2_font_size=”30px” background_layout=”dark” header_2_font_size_tablet=”” header_2_font_size_phone=”28px” header_2_font_size_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\n
Data engineers analyze and organize data, investigating patterns and discrepancies that may affect business objectives. Data engineers also use soft skills to evaluate data trends for the company and assist businesses in utilizing the collected data. Other typical data engineering tasks include: <\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”||15px||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Data acquisition” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nCollecting, analyzing, and storing data.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Patterns” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding hidden patterns in data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Procedures” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nDeveloping procedures using data<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Architecture” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nBuilding, generating, testing, and maintaining data architectures<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||||false|false” custom_padding=”15px||||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Preparation” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nPreparing data for prescriptive and predictive modeling<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Automate” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nUsing data to identify tasks that can be automated.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Strategy” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nFinding strategies to improve data quality, efficiency, and reliability.<\/p>\n[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Inform” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\nProviding updates to stakeholders using analytics<\/p>\n[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”skills” _builder_version=”4.17.6″ _module_preset=”default” background_color=”#F2F2F2″ locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nWhat skills should a Data Engineer possess?<\/strong><\/h2>\nWhile data engineers are theoretically software engineers, their capabilities go beyond what can be achieved with conventional programming skills.<\/p>\nData engineers must be familiar with these tools and skills to perform their tasks properly.<\/p>\nETL tools<\/strong>ETL<\/a> stands for extract, transform, and load. This type of tool refers to a group of data integration technologies. Low-code development platforms have largely replaced today’s traditional ETL tools. However, the ETL procedure remains crucial for data engineering in general. <\/p>\nSome of the best-known tools for this are Informatica and SAP Data Services.<\/p>\nProgramming languages used in Data Engineering<\/strong>Data engineering uses various back-end, query, and specialized languages for statistical calculations. Popular programming languages for data engineering<\/a> include Java, C#, R, Ruby, SQL, and Python. A common combination is R, Python, and SQL. <\/p>\nPython is a simple, general-purpose programming language with an extensive library. Its powerful and adaptable nature makes it ideal for ETL. ETL tasks are performed using a structured query language (SQL). <\/p>\nRelational databases play a significant role in data engineering, and SQL is the primary language for querying them. R is the premier programming language and software environment for statistical calculations and is highly favored by analysts and data miners. <\/p>\nAPIs<\/strong>Application programming interfaces (APIs) are essentially a requirement for anything related to data integration, including data engineering, of course. Every software engineering project needs APIs. They transfer data between applications and serve as a connection between those applications. <\/p>\nREST APIs are extremely important for data engineering. REST or representational state transfer APIs are excellent for any web-based tool because they can communicate over HTTP. <\/p>\nData Lakes and Data Warehouses<\/strong>Data warehouses and data lakes are massive, complex datasets that companies store for business intelligence. Business analysts process these datasets via computer clusters in business-driven information engineering. This computer network makes it easier to solve problems. <\/p>\nTwo well-known big data frameworks are Spark and Hadoop. These frameworks are used to prepare and process large datasets. They each utilize computer clusters to perform operations on massive amounts of data, such as data mining and data analysis. <\/p>\n <\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”terminology” _builder_version=”4.17.6″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Happy_customer.jpg” title_text=”Two business partners working in office” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|6px|6px|6px|6px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering terminology<\/b><\/h2>\n\nData Lake<\/a><\/li>\nData Vault<\/a><\/li>\nSnowflake<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"Data Engineering What is Data Engineering? 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[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_padding=”||15px||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Data acquisition” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\n
Collecting, analyzing, and storing data.<\/p>\n
[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Patterns” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\n
Finding hidden patterns in data<\/p>\n
[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Procedures” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\n
Developing procedures using data<\/p>\n
[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Architecture” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\n
Building, generating, testing, and maintaining data architectures<\/p>\n
[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ use_custom_gutter=”on” make_equal=”on” _builder_version=”4.17.6″ _module_preset=”default” custom_margin=”||||false|false” custom_padding=”15px||||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Preparation” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\n
Preparing data for prescriptive and predictive modeling<\/p>\n
[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Automate” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\n
Using data to identify tasks that can be automated.<\/p>\n
[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Strategy” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\n
Finding strategies to improve data quality, efficiency, and reliability.<\/p>\n
[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.17.6″ _module_preset=”default” background_color=”#FFFFFF” border_radii=”on|3px|3px|3px|3px” global_colors_info=”{}”][et_pb_blurb title=”Inform” use_icon=”on” font_icon=”||fa||900″ icon_placement=”left” _builder_version=”4.17.6″ _module_preset=”default” header_font_size=”12px” background_enable_color=”off” custom_padding=”15px|15px|15px|15px|true|true” global_colors_info=”{}”]<\/p>\n
Providing updates to stakeholders using analytics<\/p>\n
[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”skills” _builder_version=”4.17.6″ _module_preset=”default” background_color=”#F2F2F2″ locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
While data engineers are theoretically software engineers, their capabilities go beyond what can be achieved with conventional programming skills.<\/p>\n
Data engineers must be familiar with these tools and skills to perform their tasks properly.<\/p>\n
ETL tools<\/strong>ETL<\/a> stands for extract, transform, and load. This type of tool refers to a group of data integration technologies. Low-code development platforms have largely replaced today’s traditional ETL tools. However, the ETL procedure remains crucial for data engineering in general. <\/p>\nSome of the best-known tools for this are Informatica and SAP Data Services.<\/p>\nProgramming languages used in Data Engineering<\/strong>Data engineering uses various back-end, query, and specialized languages for statistical calculations. Popular programming languages for data engineering<\/a> include Java, C#, R, Ruby, SQL, and Python. A common combination is R, Python, and SQL. <\/p>\nPython is a simple, general-purpose programming language with an extensive library. Its powerful and adaptable nature makes it ideal for ETL. ETL tasks are performed using a structured query language (SQL). <\/p>\nRelational databases play a significant role in data engineering, and SQL is the primary language for querying them. R is the premier programming language and software environment for statistical calculations and is highly favored by analysts and data miners. <\/p>\nAPIs<\/strong>Application programming interfaces (APIs) are essentially a requirement for anything related to data integration, including data engineering, of course. Every software engineering project needs APIs. They transfer data between applications and serve as a connection between those applications. <\/p>\nREST APIs are extremely important for data engineering. REST or representational state transfer APIs are excellent for any web-based tool because they can communicate over HTTP. <\/p>\nData Lakes and Data Warehouses<\/strong>Data warehouses and data lakes are massive, complex datasets that companies store for business intelligence. Business analysts process these datasets via computer clusters in business-driven information engineering. This computer network makes it easier to solve problems. <\/p>\nTwo well-known big data frameworks are Spark and Hadoop. These frameworks are used to prepare and process large datasets. They each utilize computer clusters to perform operations on massive amounts of data, such as data mining and data analysis. <\/p>\n <\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”terminology” _builder_version=”4.17.6″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Happy_customer.jpg” title_text=”Two business partners working in office” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|6px|6px|6px|6px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering terminology<\/b><\/h2>\n\nData Lake<\/a><\/li>\nData Vault<\/a><\/li>\nSnowflake<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"Data Engineering What is Data Engineering? Juvo<\/span><\/li><\/ol><\/nav>Data engineering is a field that works with data to find meaning, make sense of it, and ultimately extract value from it. It is a technology-oriented approach to software engineering that supports the deployment, maintenance, and evolution of large volumes of data in a cost-effective, scalable, and efficient manner. […]<\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_seopress_titles_title":"What is Data Engineering? 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Some of the best-known tools for this are Informatica and SAP Data Services.<\/p>\n
Programming languages used in Data Engineering<\/strong>Data engineering uses various back-end, query, and specialized languages for statistical calculations. Popular programming languages for data engineering<\/a> include Java, C#, R, Ruby, SQL, and Python. A common combination is R, Python, and SQL. <\/p>\nPython is a simple, general-purpose programming language with an extensive library. Its powerful and adaptable nature makes it ideal for ETL. ETL tasks are performed using a structured query language (SQL). <\/p>\nRelational databases play a significant role in data engineering, and SQL is the primary language for querying them. R is the premier programming language and software environment for statistical calculations and is highly favored by analysts and data miners. <\/p>\nAPIs<\/strong>Application programming interfaces (APIs) are essentially a requirement for anything related to data integration, including data engineering, of course. Every software engineering project needs APIs. They transfer data between applications and serve as a connection between those applications. <\/p>\nREST APIs are extremely important for data engineering. REST or representational state transfer APIs are excellent for any web-based tool because they can communicate over HTTP. <\/p>\nData Lakes and Data Warehouses<\/strong>Data warehouses and data lakes are massive, complex datasets that companies store for business intelligence. Business analysts process these datasets via computer clusters in business-driven information engineering. This computer network makes it easier to solve problems. <\/p>\nTwo well-known big data frameworks are Spark and Hadoop. These frameworks are used to prepare and process large datasets. They each utilize computer clusters to perform operations on massive amounts of data, such as data mining and data analysis. <\/p>\n <\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”terminology” _builder_version=”4.17.6″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Happy_customer.jpg” title_text=”Two business partners working in office” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|6px|6px|6px|6px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering terminology<\/b><\/h2>\n\nData Lake<\/a><\/li>\nData Vault<\/a><\/li>\nSnowflake<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"Data Engineering What is Data Engineering? 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Python is a simple, general-purpose programming language with an extensive library. Its powerful and adaptable nature makes it ideal for ETL. ETL tasks are performed using a structured query language (SQL). <\/p>\n
Relational databases play a significant role in data engineering, and SQL is the primary language for querying them. R is the premier programming language and software environment for statistical calculations and is highly favored by analysts and data miners. <\/p>\n
APIs<\/strong>Application programming interfaces (APIs) are essentially a requirement for anything related to data integration, including data engineering, of course. Every software engineering project needs APIs. They transfer data between applications and serve as a connection between those applications. <\/p>\nREST APIs are extremely important for data engineering. REST or representational state transfer APIs are excellent for any web-based tool because they can communicate over HTTP. <\/p>\nData Lakes and Data Warehouses<\/strong>Data warehouses and data lakes are massive, complex datasets that companies store for business intelligence. Business analysts process these datasets via computer clusters in business-driven information engineering. This computer network makes it easier to solve problems. <\/p>\nTwo well-known big data frameworks are Spark and Hadoop. These frameworks are used to prepare and process large datasets. They each utilize computer clusters to perform operations on massive amounts of data, such as data mining and data analysis. <\/p>\n <\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”terminology” _builder_version=”4.17.6″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Happy_customer.jpg” title_text=”Two business partners working in office” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|6px|6px|6px|6px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering terminology<\/b><\/h2>\n\nData Lake<\/a><\/li>\nData Vault<\/a><\/li>\nSnowflake<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"Data Engineering What is Data Engineering? 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REST APIs are extremely important for data engineering. REST or representational state transfer APIs are excellent for any web-based tool because they can communicate over HTTP. <\/p>\n
Data Lakes and Data Warehouses<\/strong>Data warehouses and data lakes are massive, complex datasets that companies store for business intelligence. Business analysts process these datasets via computer clusters in business-driven information engineering. This computer network makes it easier to solve problems. <\/p>\nTwo well-known big data frameworks are Spark and Hadoop. These frameworks are used to prepare and process large datasets. They each utilize computer clusters to perform operations on massive amounts of data, such as data mining and data analysis. <\/p>\n <\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”terminology” _builder_version=”4.17.6″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/juvo.be\/wp-content\/uploads\/Happy_customer.jpg” title_text=”Two business partners working in office” _builder_version=”4.17.6″ _module_preset=”default” border_radii=”on|6px|6px|6px|6px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nData engineering terminology<\/b><\/h2>\n\nData Lake<\/a><\/li>\nData Vault<\/a><\/li>\nSnowflake<\/a><\/li>\n<\/ul>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"Data Engineering What is Data Engineering? Juvo<\/span><\/li><\/ol><\/nav>Data engineering is a field that works with data to find meaning, make sense of it, and ultimately extract value from it. It is a technology-oriented approach to software engineering that supports the deployment, maintenance, and evolution of large volumes of data in a cost-effective, scalable, and efficient manner. […]<\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_seopress_titles_title":"What is Data Engineering? How do you implement it? | Juvo","_seopress_titles_desc":"Data engineering is a field that works with data to find meaning, make sense of it, and ultimately extract value from it.","_seopress_robots_index":"","_seopress_robots_follow":"","_seopress_robots_imageindex":"","_seopress_robots_snippet":"","_seopress_robots_primary_cat":"","_seopress_robots_breadcrumbs":"","_seopress_robots_freeze_modified_date":"","_seopress_robots_custom_modified_date":"","_seopress_robots_canonical":"","_seopress_social_fb_title":"","_seopress_social_fb_desc":"","_seopress_social_fb_img":"","_seopress_social_fb_img_attachment_id":0,"_seopress_social_fb_img_width":0,"_seopress_social_fb_img_height":0,"_seopress_social_twitter_title":"","_seopress_social_twitter_desc":"","_seopress_social_twitter_img":"","_seopress_social_twitter_img_attachment_id":0,"_seopress_social_twitter_img_width":0,"_seopress_social_twitter_img_height":0,"_seopress_redirections_value":"","_seopress_redirections_enabled":"","_seopress_redirections_enabled_regex":"","_seopress_redirections_logged_status":"both","_seopress_redirections_param":"","_seopress_redirections_type":301,"_seopress_analysis_target_kw":"","_seopress_news_disabled":"","_seopress_video_disabled":"","_seopress_video":[],"_seopress_pro_schemas_manual":[],"_seopress_pro_rich_snippets_disable_all":"","_seopress_pro_rich_snippets_disable":[],"_seopress_pro_schemas":[],"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","content-type":"","footnotes":""},"class_list":["post-41596","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/pages\/41596","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/comments?post=41596"}],"version-history":[{"count":1,"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/pages\/41596\/revisions"}],"predecessor-version":[{"id":41599,"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/pages\/41596\/revisions\/41599"}],"wp:attachment":[{"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/media?parent=41596"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Two well-known big data frameworks are Spark and Hadoop. These frameworks are used to prepare and process large datasets. They each utilize computer clusters to perform operations on massive amounts of data, such as data mining and data analysis. <\/p>\n
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Data Engineering What is Data Engineering? Juvo<\/span><\/li><\/ol><\/nav>Data engineering is a field that works with data to find meaning, make sense of it, and ultimately extract value from it. It is a technology-oriented approach to software engineering that supports the deployment, maintenance, and evolution of large volumes of data in a cost-effective, scalable, and efficient manner. […]<\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_seopress_titles_title":"What is Data Engineering? How do you implement it? | Juvo","_seopress_titles_desc":"Data engineering is a field that works with data to find meaning, make sense of it, and ultimately extract value from it.","_seopress_robots_index":"","_seopress_robots_follow":"","_seopress_robots_imageindex":"","_seopress_robots_snippet":"","_seopress_robots_primary_cat":"","_seopress_robots_breadcrumbs":"","_seopress_robots_freeze_modified_date":"","_seopress_robots_custom_modified_date":"","_seopress_robots_canonical":"","_seopress_social_fb_title":"","_seopress_social_fb_desc":"","_seopress_social_fb_img":"","_seopress_social_fb_img_attachment_id":0,"_seopress_social_fb_img_width":0,"_seopress_social_fb_img_height":0,"_seopress_social_twitter_title":"","_seopress_social_twitter_desc":"","_seopress_social_twitter_img":"","_seopress_social_twitter_img_attachment_id":0,"_seopress_social_twitter_img_width":0,"_seopress_social_twitter_img_height":0,"_seopress_redirections_value":"","_seopress_redirections_enabled":"","_seopress_redirections_enabled_regex":"","_seopress_redirections_logged_status":"both","_seopress_redirections_param":"","_seopress_redirections_type":301,"_seopress_analysis_target_kw":"","_seopress_news_disabled":"","_seopress_video_disabled":"","_seopress_video":[],"_seopress_pro_schemas_manual":[],"_seopress_pro_rich_snippets_disable_all":"","_seopress_pro_rich_snippets_disable":[],"_seopress_pro_schemas":[],"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","content-type":"","footnotes":""},"class_list":["post-41596","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/pages\/41596","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/comments?post=41596"}],"version-history":[{"count":1,"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/pages\/41596\/revisions"}],"predecessor-version":[{"id":41599,"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/pages\/41596\/revisions\/41599"}],"wp:attachment":[{"href":"https:\/\/juvo.be\/en\/wp-json\/wp\/v2\/media?parent=41596"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}