Data Engineering
What is Data Engineering?
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.
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.
Data Engineering for beginners
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:
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What you will learn about data engineering
Data engineering techniques
In data engineering, many different techniques are available. We will guide you through each technique.
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Types of Data Engineering
Every company requires a different Data Engineering solution depending on its goals. Therefore, several types of Data Engineering exist:
Big data engineering
Big Data Engineering is the process of setting up, developing, and maintaining an infrastructure for processing, storing, and analyzing large volumes of data, also known as “big data.”
Cloud data engineering
Cloud data engineering involves designing, building, and maintaining systems for the storage, processing, and analysis of data in a cloud computing environment.
Learning data engineering
Juvo regularly organizes webinars and info sessions on data engineering. We guide you through the latest developments and techniques and answer your questions.
Data Engineering Platforms & Tools
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:
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Data engineering programming
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.
Data engineering news
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What does a data engineer do?
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.
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.
With the rise of big data and analytics, all roles within the field of data engineering have become highly popular.
Working as a Data engineer
Looking to build a career as a Data Engineer? At Juvo, you will find the most challenging Data Engineer jobs.
The importance of data engineering
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:
- Finding best practices to improve the software development lifecycle and assisting in their implementation.
- Improving information security and protecting the company against online attacks.
- Increasing knowledge of the business domain
Data Engineering process
What is it?
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.
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.
Tasks of a Data Engineer
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:
Data acquisition
Collecting, analyzing, and storing data.
Patterns
Finding hidden patterns in data
Procedures
Developing procedures using data
Architecture
Building, generating, testing, and maintaining data architectures
Preparation
Preparing data for prescriptive and predictive modeling
Automate
Using data to identify tasks that can be automated.
Strategy
Finding strategies to improve data quality, efficiency, and reliability.
Inform
Providing updates to stakeholders using analytics
What skills should a Data Engineer possess?
While data engineers are theoretically software engineers, their capabilities go beyond what can be achieved with conventional programming skills.
Data engineers must be familiar with these tools and skills to perform their tasks properly.
ETL tools
ETL 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.
Some of the best-known tools for this are Informatica and SAP Data Services.
Programming languages used in Data Engineering
Data engineering uses various back-end, query, and specialized languages for statistical calculations. Popular programming languages for data engineering include Java, C#, R, Ruby, SQL, and Python. A common combination is R, Python, and SQL.
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).
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.
APIs
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.
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.
Data Lakes and Data Warehouses
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.
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.
