Five common Databricks coding patterns — including undocumented API calls, manual SparkSession instantiation, and hardcoded Spark configs — that pass code review but fail silently in serverless environments or during platform migrations. For each anti-pattern, this post explains why it breaks and shows the correct native Databricks approach using DABS, the Databricks SDK, and dynamic job parameters.

Still running ADF after moving to Databricks? Here's why it happens, what it's costing your governance story, and how Lakeflow Jobs closes the gap.

Use Databricks Workflow backfill jobs to reprocess historical data, recover from outages, and handle late-arriving data efficiently.