Azure Databricks and Microsoft Fabric overlap more every quarter. This comparison covers pricing models, throttling, governance and the mirrored-catalog pattern that lets a mid-market team use both without paying for engineering twice.
Replace the legacy EDW with a governed Databricks lakehouse. A staged, low-drama exit path that cuts cost, fixes pipelines, ships AI.
Databricks Workflows is now Lakeflow Jobs, and the old Workflows vs Airflow question has a third contender in Dagster. Here is how the three compare for a lakehouse team, when a hybrid makes sense, and the red flags that say fix the process before you pick a tool.
Security tool sprawl is a data engineering problem. Here is how a Databricks lakehouse gold layer replaces point tools and legacy SIEMs.
Map raw security logs to OCSF in the Databricks lakehouse silver layer to end schema drift, cut SIEM ingest cost and speed threat hunting.
New Databricks workspaces lose Hive metastore access from 30 September 2026, while existing ones keep it. Here is how to plan the Unity Catalog migration, pick the right tool per table, and avoid the breaks.
Policy binders do not stop bad data. See how governance coded into a Databricks lakehouse keeps AI pipelines and outputs trustworthy.
Databricks Lakewatch is an agentic SIEM that runs on the lakehouse, built on OCSF and Unity Catalog. Here is what it changes for security data, and what it does not.
Bronze, silver, and gold are process control for your Databricks lakehouse. Here is how layered data engineering makes pipelines AI-ready.