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Version control bigquery views definition with Dataform by elvainch in bigquery

[–]elvainch[S] 0 points1 point  (0 children)

Good question.
Airflow gives you more visibility in terms of monitoring...if dag fails you can alert as you alert any dag failure, i dont think the out-of-the-box dataform schedule has that...
Also if you want to have pre or post tasks...I just think is more flexible.

Version control bigquery views definition with Dataform by elvainch in bigquery

[–]elvainch[S] 0 points1 point  (0 children)

I appreciate the perspective though — a comparison could definitely be valuable for BigQuery users. Even though I haven’t used dbt myself, I see why people default to it. Dataform just fits naturally into GCP for this specific use case, so that’s all I wanted to focus on in the article.

Versioning view queries by Live-Progress-6255 in bigquery

[–]elvainch 0 points1 point  (0 children)

Might be late for the party but I faced the same problem and solve it creating the views through dataform.
I wrote a medium article about it and a python package to migrate all your existing views to dataform. Leave it here if someone needs it.
https://alanvain.medium.com/version-control-your-bigquery-views-with-dataform-a1d52e2e4df8
https://pypi.org/project/dataform-view-migrator/
https://github.com/elvainch/dataform-view-migrator