Data & AI Engineering

Is Databricks worth it? Seven reasons the lakehouse pays off, four cases to pass, a 10-question self-check and a 90-day start plan.

Databricks limitations come from defaults, not the lakehouse. Nine problems we hit, from serverless timeouts to idle compute, with fixes.

Microsoft Sentinel’s data lake tier, priced meter by meter from Microsoft’s published rates, set against the analytics tier and a lakehouse in a worked 100 GB a day example.

Power BI is swapping the Simba ODBC driver for Databricks ADBC. See the 2026-2027 dates, what breaks and a four-step migration checklist.

The AICPA has not written AI-specific SOC 2 criteria. Auditors apply the existing Trust Services Criteria to agents, and a governed lakehouse already produces much of the evidence they ask for.

Databricks Apps run governed Python tools in your lakehouse. Learn the best uses, 5 cache layers behind stale data and the sharing limit.

SIEM vs security data lake: a siloed SIEM misses fraud that crosses identity, ERP and CRM. A Databricks lakehouse joins them in one query.

Every AI agent acts through a non-human identity: a service principal, a token or a secret. On a lakehouse, that identity is the perimeter, and it needs the same inventory and access review as a person.

KPI reconciliation fixes dashboards that never match finance: one written definition per metric, tested lakehouse logic and named owners.