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The goal is to reliably and efficiently release data into production
Data Pipelines are series of tasks organised in aor “DAG”. Historically, these are run on open-source workflow orchestration packages like or , and require i managed by data engineers or platform teams. These data pipelines typically run on a , and allow data engineers to update data in locations such as data warehouses or data lakes.
This is now changing. There is ahappening. As the data engineering industry matures, mindsets are shifting from a “move data to serve the business at all costs” mindset to “reliability and efficiency” / “software engineering” mindset.
Continuous Data Integration and Delivery
I’ve written before about howwhereas software teams ship code.
This is a process called “Continuous Data Integration and Delivery”, and is the process of reliably and efficiently releasing data into production. There are subtle differences with the definition of “” as used in Software Engineer, illustrated below.
In software engineering, Continuous Delivery is non-trivial because of the importance of having afor code to operate in a staging environment.
Within Data Engineering, this is not necessary because the good we ship is data. If there is a table of data, and we know that as long as a few conditions are satisfied, the data is of a sufficient quality to be used, then that is sufficient for it to be “released” into production, so to speak.
The process of releasing data into production — the analog for Continuous Delivery — is very simple, as it simply relates to copying ora dataset.
Furthermore, a key pillar of data engineering is reacting to new data as it arrives or checking to see if new data exists. There is no analog for this in software engineering — software applications do not need to…
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