Deploy Your Google Cloud Functions The Right Way (Step-by-Step Guide)

Three years ago (at the time of publication), I deployed my first cloud function two hours before the end of a Friday work day. As if that wasn’t cutting it close enough, it was also the weekend before my wedding. The function was simple enough; it made two requests and loaded two reports into our data warehouse in BigQuery. The trickiest part is that it required a nested schema. Also, I had never followed all of these deployment steps before, only having experience developing and running code locally.

Since then, I’ve developed, maintained and deployed nearly 200 cloud functions in 3 years. And, over that time frame, I’ve developed something of a process I intend to share.

Previously, I’ve explained the “big picture” of deployment.

As well as the importance of testing and ongoing challenges of package compatibility going from staging to production environments.

In my original post, I glossed over the “how”, assuming that surely someone in the data-verse has created documentation that covers a similar process I use.

However, when searching for resources recently, I was shocked to find that while the components of the process exist in the form of Google Cloud docs and a GitHub Actions repo, I couldn’t find anything comprehensive.

So I decided to make the resource I wished Google would have suggested.

From beginning to end, this is how a professional data engineer deploys a gen. 1 cloud function on the Google Cloud Platform.

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