Built-in dashboards plus Genie, a natural-language interface for business users to query governed data.
Databricks Profiled
A lakehouse platform built on open formats (Delta Lake, Apache Iceberg) and Apache Spark, with unified governance through Unity Catalog.
At a glance
Databricks created the lakehouse approach: warehouse-grade reliability and SQL performance on open table formats in cloud object storage. It began as a managed Apache Spark service and now covers ingestion, pipelines, SQL warehousing, BI, ML and GenAI, all governed by Unity Catalog. It runs on all three major clouds.
Where it fits
- Strongest when: you want open formats, multi-cloud, heavy data engineering or ML/AI workloads, and code-first teams.
- Consider carefully when: the team wants a no-ops SaaS experience with minimal tuning. Serverless options narrow this gap, but check that they're available in your region.
Key concepts
- Account → Workspace → Unity Catalog metastore → catalog.schema.table
- Classic vs serverless compute: who owns the VMs and how you're billed
- Medallion architecture: bronze, silver and gold layers, a common modelling pattern
- Open by default: Delta Lake, Delta Sharing, MLflow and Unity Catalog are all open source
In the tool landscape (5)
- Databricks — Lakehouse platform built on Apache Spark and open table formats, governed through Unity Catalog.
- Databricks Asset Bundles — Defines Databricks jobs, pipelines and other workspace assets as YAML and deploys them with the Databricks CLI. Renamed Declarative Automation Bundles in 2026.
- Delta Lake — Open table format that adds ACID transactions, time travel and schema enforcement to data lakes.
- MLflow — Open-source platform for experiment tracking, model registry and GenAI evaluation, created by Databricks.
- Unity Catalog — Governance catalogue for data and AI assets, available as open source and managed in Databricks.
Products (12)
BI & Analytics
Data Apps & Development
Hosts data and AI applications (e.g. Streamlit, Dash, Gradio) directly on the Databricks platform.
Data Integration (ETL/ELT)
Databricks' data engineering suite for ingestion, declarative pipelines and orchestration.
Data Science & ML
A hosted version of open-source MLflow for experiment tracking, model registry (in Unity Catalog) and GenAI evaluation.
Databricks' AI tooling for building, serving and monitoring ML models and GenAI agents.
Data Sharing & Marketplace
A privacy-safe environment for multiple parties to collaborate on joint data without exposing raw data.
A marketplace for datasets, notebooks, models and solutions, built on Delta Sharing.
An open protocol for securely sharing live data with any computing platform, not only Databricks.
Data Warehouse
SQL warehouses (serverless, pro and classic) for BI and analytical workloads on the lakehouse.
Governance & Catalog
Unified governance for data and AI assets (tables, volumes, models, functions), with access control, lineage and auditing across workspaces.
Lakehouse
An open-source table format that adds ACID transactions, time travel and schema enforcement to Parquet on object storage.
Operational Database
Preview
A managed, Postgres-compatible operational database integrated with the lakehouse and Unity Catalog.
Upcoming events
Conference · In person
Databricks Data + AI World Tour — Amsterdam
Tue, 10 Nov 2026
Amsterdam, Netherlands
- Free
Conference · In person
Databricks Data + AI World Tour - Atlanta
Tue, 10 Nov 2026
Atlanta, United States
- Free
Conference · In person
Databricks Data + AI World Tour — London
Wed, 11 Nov 2026
London, United Kingdom
- Free
From the blog
Data architecture patterns: what actually competes with what
6 October 2026
Lambda, Kappa, Lakehouse, Mesh, Medallion. They get set out side by side as though the job is to pick one. They answer different questions, and a real platform runs several at once.
Data sovereignty in cloud data platforms: what to check before you choose
5 August 2025
Residency is not sovereignty. A practical guide to the legal, operational and architectural questions to ask of Microsoft Fabric, Databricks, Snowflake, and any other cloud data platform.
Capability snapshot
29 native · 1 partial
| Capability | Support | Notes |
|---|---|---|
| Platform & Deployment | ||
| Cloud availability | Native | AWS, Azure (Azure Databricks, a first-party Azure service) and Google Cloud. |
| Deployment model | Native | Control plane run by Databricks; classic compute runs in the customer's cloud account, and serverless compute runs in Databricks' account. |
| Pricing model | Native | DBU-based. |
| Storage & Formats | ||
| Open table formats | Native | Delta Lake native; Apache Iceberg supported through managed Iceberg tables and UniForm. Verify the current state. |
| Storage/compute separation | Native | |
| Query external data in place | Native | Lakehouse Federation queries external databases; external locations reach cloud storage. |
| Compute & Workloads | ||
| SQL warehousing | Native | |
| Serverless compute | Native | Serverless SQL warehouses, notebooks, jobs and pipelines. |
| Spark & notebooks | Native | Databricks was founded by the original creators of Apache Spark. |
| Languages | Native | SQL, Python, Scala, R. |
| Integration & Pipelines | ||
| Managed ingestion connectors | Native | Lakeflow Connect managed connectors; Auto Loader for files. |
| Declarative transformation pipelines | Native | Lakeflow Declarative Pipelines (formerly Delta Live Tables). |
| Orchestration | Native | Lakeflow Jobs (formerly Databricks Workflows). |
| Streaming / real-time | Native | Spark Structured Streaming; streaming tables in declarative pipelines. |
| Governance & Security | ||
| Unified catalog | Native | |
| Lineage | Native | Column-level lineage captured automatically in Unity Catalog. |
| Row/column-level security | Native | Row filters and column masks in Unity Catalog. |
| Sharing & Collaboration | ||
| Cross-organisation data sharing | Native | Delta Sharing, an open protocol that also shares to non-Databricks consumers. |
| Data marketplace | Native | |
| Data clean rooms | Native | |
| Analytics, ML & AI | ||
| Native BI / dashboards | Native | AI/BI Dashboards and Genie. |
| ML lifecycle | Native | Managed MLflow, Feature Store, Model Serving. |
| LLM functions in SQL | Native | AI Functions (e.g. ai_query) callable from SQL. |
| Natural-language querying / data agents | Native | AI/BI Genie spaces; Agent Bricks for building agents. |
| Operational (OLTP) database | Partial | Lakebase (managed Postgres). Verify GA status. |
| Unification | ||
| Lifecycle coverage | Native | Covers integration, data, metadata, quality, AI/ML and governance with its own components; visualisation is the thinnest, with dashboards rather than a full BI tool. |
| Shared storage | Native | One lakehouse store on the customer's cloud object storage, read by every engine in open formats rather than copied per workload. |
| Shared governance | Native | Unity Catalog "operates beneath every data and AI interaction" automatically, enforcing access control on a table query or a model call alike, and tracking lineage and audit across both. |
| Shared metadata and identity | Native | Unity Catalog is one catalogue across data and AI assets, enabled automatically for workspaces created after 8 November 2023; older workspaces need an upgrade. |
| Sold as one platform | Native | One platform billed in DBUs across workloads rather than as separately licensed products. |