Data Engineering is the foundation for the new world of Big Data. Data engineers design and build pipelines that allow for the collection of data from multiple sources, with the goal of enabling data scientists to derive and deliver big data insights. Data engineers move, remodel, and manage data sets from 10s if not 100s of internal company applications so analysts and data scientists don’t need to spend their time constantly pulling data sets. Data engineers are valued for transforming data into usable form.
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).
