Go From Staging Table To Production Data With 1 Stupid Simple SQL Line
A risk-averse approach to “flipping the switch” from test tables to production tables featuring a subtle BigQuery SQL function.
Data engineering tutorials don’t talk enough about the s-word. Not the four-letter curse you reflexively blurt out when your pull request breaks production. Or what you mutter when your schema doesn’t match the target table.
I’m talking about staging.
Because for all the questions I get from stakeholders that want to know: “When will (data source) be available in production?” I stop them and say, “Well, first it will need to load to our staging tables.”
And, at the risk of over-clarifying, a staging environment is not just a sandbox where you throw your spaghetti code to see what sticks. It’s a data payload’s last stop before we “flip the switch” and point our pipelines at production end points, tables and dashboards.
Ideally, your staging environment will mimic your production table and still maintain high data engineering standards like:
- Clustering
- Partitioning
- De-duplicated
In this way, a staging table represents your final draft that your manager, team lead or stakeholder will review before saying “We’re confident this data can be delivered in this form at this time.”
And while scrutinizing a staging table can be an opportunity to refine table characteristics like schemas and pipeline criteria like load cadence, it is, above all, a time to minimize errors when it’s time to flip that switch.
While I’m sure you’d like to absorb 800 more words about staging tables, my goal is to share a strategy that can make that staging→production transition seamless.
And it’s only 1 line of BigQuery SQL with a command you probably didn’t know existed.
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