What is Databricks?
Databricks describes a lakehouse architecture connecting processing and analytics over shared data. The managed platform serves several workloads, but compute choices, storage design and governance remain customer decisions. An open source foundation does not mean that the managed service is free.
Read the full overview
Official sources checked 27 September 2026: lakehouse architecture, platform scope.
DatabricksData Warehousing & LakehouseLakehouse platform for data and AI workloads
Key capabilities
- Data engineering workflows
- SQL and analytical workloads
- Machine learning on the platform
Where Databricks fits
Map the path from source to governed table to consumer. Define schema ownership, compute policies and retention before migrating a major workload.
Positioning and limitations
Shortlist Databricks when engineering and analytics need common datasets. Prototype ingestion, transformation and production queries, then inspect permissions and cost attribution.
A shared platform can reduce duplicated plumbing while concentrating spend and access decisions. Make both visible in the pilot.
“Lakehouse platform for data and AI workloads”
Technical details
- Access
- See vendor
- Source model
- Commercial
- License
- Commercial
- Founded
- 2013
- Headquarters
- San Francisco, USA
- Pricing model
- Paid
- API
- Available
- Website
- www.databricks.com ↗
What to verify
Start from the users, systems and operating responsibilities the tool needs to support.
- Confirm current features, licensing and support terms.
- Validate deployment, data location and access control.
- Test integrations, exports and recovery workflows.
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