SQL Tricks For Data Scientists

SQL Tricks For Data Scientists

Leveraging SQL for the win. I found myself buried in cron jobs and CSV files that were requested by various teams within my company.

I found myself buried in cron jobs and CSV files that were requested by various teams within my company. There were endless requests for new data exports or updates to those exports. Anytime anybody wanted to add a field, I was the single point of failure for that task. I had to first remember which service generated that report as well as remember the point of the report. Then I had to investigate if the new field that was desired was available, could be derived from other columns, or required a new database connection.

I needed to find an application that could help me to keep track of all the reports, manage all the various database connections, and allow someone to maintain the notifications on their own. A final important feature would be to offload some of the report generating off of my plate and allow people to self-serve all the data.

I settled on Metabase because it fit all the criteria I was looking for. It’s open-source, works with a variety of different data sources, has user/permission management, many charting/dashboarding options, and various different types of notifications.

There was only one problem — Metabase is entirely SQL based. My workflow of using simple selects to query the database and transforming the data into CSVs wasn’t going to be available. I had to use raw SQL. How could I insert any logic into SQL? How could I loop over results? How could I generate date ranges? How can I use rolling windows? Those types of questions made it sound like a SQL-only workflow wasn’t going to cut it.

But what if those operations were actually possible? What if SQL was actually a Turing-complete language with recursion? What if there was a way to pivot data or use windows? Below I’ll go over a few tips that I discovered on my journey to take full advantage of the power of SQL.

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