Data engineering has many facets. One of the most common projects a data engineer takes on is developing an ETL pipeline from an operational DB to a data warehouse. Our team wanted to cover the overarching design of an ETL.

What are the typical principal components, stages, considerations, etc?
We started this by first writing Creating An ETL Part 1(more to come)

and we and now have worked on a video that is below that walks through the process. We wanted to discuss why each stage is important and what occurs when data goes from raw to stage, why do we need a raw database and so on.

Data engineering is a complex discipline that partners automation, programming, system design, databases, and analytics in order to ensure that analysts, data scientists and end-users have access to clean data.

This all starts with the basic ETL design.

We are practicing up for a webinar that we will be hosting on ETL development with Python and SQL. The webinar itself will be much more technical and dive much deeper into each component described in the video. However, we wanted to see how using the whiteboard was like in case we need it.

If you enjoyed this video, check out some of my other top videos.

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About me:
I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.

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