Data Engineering

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ericjmorey

(2017) Rise of the Data Engineer (medium.com)

submitted 1 year ago by Reader9 to c/data_engineering

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This article helped defined the “data engineer” role so I’d say it belongs here!

Although some time has passed, I find it very relevant: SQL is used more than ever, graphical ETL tools that don’t output code are rare and vendors are still trying to convince executives to trust all their data to proprietary data warehouses.

The author Maxime Beauchemin also wrote Airflow and Superset so they have some experience worth listening to.

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[–] Reader9 1 points 1 year ago

those tools eventually became what Airflow and other orchestration tools are: defining DAGs and running scripts

Definitely. It is much more pleasant to work with better tools for the same functionality.

Airflow got a lot of things right. For example in Luigi a runnable “task” is a python class that gets implicitly executed, whereas in Airflow tasks are made from functions that get called in a more straightforward/imperative manner. This makes DAGs much easier to read and write in Airflow.