All About Data Engineering

Data engineering is a vital part of today's technology-driven world, but not everyone understands what data engineers do. However, the work of data engineers is crucial to help businesses of all sizes succeed. The world is filled with data, and it's easier than ever to collect more; data engineers make sense of it all, turning data into insights.

Understanding Data Engineering

Also referred to as information engineering, data engineering is all about using software and analytical techniques to create powerful information systems. It deals with gathering, transforming, and managing different types of data from multiple sources and using it to gain actionable insights. Data engineers can employ a range of tools to integrate and analyze data, including artificial intelligence. They then examine how to apply what they learn to the real world.

How Is Data Engineering Important?

Data engineering is an integral part of many businesses because it allows them to turn data into information that can guide their actions. For instance, collecting and analyzing survey data about a product can allow the manufacturer to make improvements that will lead to more sales. Closely examining data about a website's performance may uncover security flaws that can be fixed before a cyberattack happens. And bringing together a variety of data about a service that isn't selling well can help to explain whether the service itself needs changing or its marketing does.

As factors like cloud computing and open-source development increase the amount of available data and the ease of collecting it, skilled data engineers are more important than ever. These are the professionals who can turn a flood of data into an effective and efficient system.

How Does Data Engineering Differ From Data Science?

Data engineering and data science are two different but related roles that can have some overlap between them. Data engineers are more often found at the beginning of the workflow, creating the architecture that collects and organizes reliable data. Data scientists analyze and interpret this data to spot trends and pull out important details that can lead to action.

Machine learning engineers may take on the roles of both data scientists and data engineers, while advanced data engineers may also assume some responsibilities of machine learning engineers. However, as big data projects often fail due to inaccurate data structures, it is essential to have a separate data engineering team. This way, they can focus on the specific tasks that fall within their area of expertise.

The Role of the Data Engineer

Data engineering is a broad field with different roles requiring specific skill sets. A generalist data engineer tackles end-to-end data collection but may not be an expert in system architecture. Pipeline-centric data engineers excel at designing and managing complex data pipelines that bring together data streams from multiple sources. And database-centric data engineers working on analytics databases and closely collaborate with data scientists to ensure the optimal performance of data warehouses.

Skills That Data Engineers Need

Data engineers are tech professionals with a specialized set of skills that go beyond general programming. To be successful, data engineers need to have a solid understanding of ETL tools like Informatica and SAP Data Services as well as expertise in programming languages such as Python, Java, C#, and Ruby. APIs (application programming interfaces) are also essential for data integration and the communication of data between applications. They typically use REST APIs, which communicate over HTTP, to facilitate this exchange. Additionally, data engineers must be familiar with frameworks like Spark and Hadoop to properly manage large datasets that are stored in data warehouses and data lakes.

Why Businesses Need Data Engineering

The insights gained from data engineers' work can be used to drive the success of a business. With data engineering, businesses have access to valuable information that's readable and usable, giving them an edge over competitors who lack this key insight.

Additional Resources

• Understanding Data Engineering
• What Is Data Science?
• The Difference Between Machine Learning and Data Science
• Data Engineering Tools You Should Know About
• What Does a Data Engineer Do?
• Skills You Need to Become a Data Engineer
• Real-World Applications of Machine Learning
• Data Engineering Best Practices
• Data Warehouses and How They Work
• Data-Driven Experience Management
• How to Become a Data Engineer
• Data Engineering and How it Affects Business
• What Are Data Structures, and How Do They Work?
• Data Research Tools
• What Is a Data Lake?
• What Is an API?