Data engineering is part of the big data ecosystem and is closely linked to data science. Data engineers work in the background and do not get the same level of attention as data scientists, but they are critical to the process of data science. The roles and responsibilities of a data engineer vary depending on an organization's level of data maturity and staffing levels; however, there are some tasks, such as the extracting, loading, and transforming of data, that are foundational to the role of a data engineer.

At the lowest level, data engineering involves the movement of data from one system or format to another system or format. Using more common terms, data engineers query data from a source (extract), they perform some modifications to the data (transform), and then they put that data in a location where users can access it and know that it is production quality (load). The terms extract, transform, and load will be used a lot throughout this...

Data engineering is what makes data science possible. Again, depending on the maturity of an organization, data scientists may be expected to clean and move the data required for analysis. This is not the best use of a data scientist's time. Data scientists and data engineers use similar tools (Python, for instance), but they specialize in different areas. Data engineers need to understand data formats, models, and structures to efficiently transport data, whereas data scientists utilize them for building statistical models and mathematical computation.

Data scientists will connect to the data warehouses built by data engineers. From there, they can extract the data required for machine learning models and analysis. Data scientists may have their models incorporated into a data engineering pipeline. A close relationship should exist between data engineers and data scientists. Understanding what data scientists need in the data will only...

To build data pipelines, data engineers need to choose the right tools for the job. Data engineering is part of the overall big data ecosystem and has to account for the three Vs of big data:

• Volume: The volume of data has grown substantially. Moving a thousand records from a database requires different tools and techniques than moving millions of rows or handling millions of transactions a minute.
• Variety: Data engineers need tools that handle a variety of data formats in different locations (databases, APIs, files).
• Velocity: The velocity of data is always increasing. Tracking the activity of millions of users on a social network or the purchases of users all over the world requires data engineers to operate often in near real time.

Programming languages

The lingua franca of data engineering is SQL. Whether you use low-code tools or a specific programming language, there is almost no way to get around knowing SQL. A strong foundation...

In this chapter, you learned what data engineering is. Data engineering roles and responsibilities vary depending on the maturity of an organization's data infrastructure. But data engineering, at its simplest, is the creation of pipelines to move data from one source or format to another. This may or may not involve data transformations, processing engines, and the maintenance of infrastructure.

Data engineers use a variety of programming languages, but most commonly Python, Java, or Scala, as well as proprietary and open source transactional databases and data warehouses, both on-premises and in the cloud, or a mixture. Data engineers need to be knowledgeable in many areas – programming, operations, data modeling, databases, and operating systems. The breadth of the field is part of what makes it fun, exciting, and challenging. To those willing to accept the challenge, data engineering is a rewarding career.

In the next chapter, we will begin by setting...