Write Your First ETL Pipeline (Part III)
Take a SQL script from a SQL environment to Google Cloud Platform by introducing a dynamic data check and upload step.
As a preface, I thought the prior two installments in this series, Part I and Part II were a fair representation of building an ETL data pipeline.
But first, if you’re unfamiliar with this series of walkthroughs, here are links to part I and part II published in another publication I edit, Learning SQL.
Beyond the code that moves the data between source and storage, there is really only one final step: Deployment.
While I’ve discussed deployment both as an abstract concept, and with examples, after re-reading the Write Your First Pipeline series, I thought it would be helpful to add a deployment step to “bring it all together.”
The other advantage of discussing deployment in reference to this particular script is that this is a SQL-based pipe. That means the only dependencies we need to worry about are the data sources our statements reference, not external packages, like you might encounter in Python.
That fact makes this build easier, but the variety of upstream data sources also brings its own challenges.
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