A practical data quality framework for the Databricks lakehouse: where Lakeflow expectations, DQX and Great Expectations fit, and how DMAIC turns rules into a control system.
Lakeflow Connect gives the Databricks lakehouse managed, serverless ingestion for common SaaS apps and databases. Here is how it works, where it stops, and how to decide between a managed connector and your own build.
A mid-market playbook for Teradata to Databricks migration: sizing the estate, converting BTEQ and stored procedures, moving data with TPT, reconciling results and knowing when you are ready to cut over.
Azure Data Factory and Databricks overlap more than they used to. This comparison shows what each does best, how each bills, and when a mid-market Azure team should consolidate orchestration on Lakeflow.
A security data lake is a data engineering build, not a product purchase. Here is how to land raw logs, normalise them to OCSF and govern them on the Databricks lakehouse.
A practical Snowflake to Databricks migration plan for mid-market and enterprise teams: what to inventory, how to convert SQL, how to run both platforms in parallel and when to cut over.
Delta Live Tables is now Lakeflow Declarative Pipelines. Your DLT code still runs, APPLY CHANGES now has a newer name in AUTO CDC, and the real work is cleaning up the process around your pipelines, not the code.
Databricks pricing is a DBU meter plus a cloud bill underneath it. Here is how the pieces fit, where lakehouse budgets go wrong, and what to ask before you commit.
Most ETL tool rankings assume a warehouse target. This comparison looks at eight options through a lakehouse lens: where each one runs, how it bills, and where it breaks.