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02-18-2026 12:31 AM
Hello,
In this article: https://www.databricks.com/blog/from-airflow-to-lakeflow-data-first-orchestration
I understand that if I want to create and deploy ml pipeline in production the recommandation is to use databricks asset bundles.
But by using it and databricks sdk the second one is easier to write. Why not directly using databricks sdk for creating ml pipeline using lakeflow jobs and using it also in prod ?
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02-18-2026 01:30 AM
Hi @Dali1 ,
When you deploy with Asset Bundles, DABk keeps track of what’s already been deployed and what has changed. That means:
it only updates what needs updating,
detects drift between your desired state and the workspace,
lets you generate plans/diffs,
and reduces deployment errors.
It you've worked with Terraform is the same concept (in fact, under the hood DABs are using terraform).
SDK calls by themselves are stateless: if you run the same API calls over and over, you’re responsible for tracking what exists or changed - this becomes complex as your pipelines grow.
