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Databricks

About Databricks

Databricks is a data and AI company offering the Data + AI Platform. It is built on an open lakehouse architecture that provides a unified foundation for data and governance. Technical teams can build and deploy data and AI apps and products on the platform. Databricks was founded by the original creators of Apache Spark, Delta Lake, MLflow and Unity Catalog.

Products

  • Lakebase: A serverless Postgres database integrated with the lakehouse.
  • Genie: Provides data-aware AI assistance for insights and work.
  • Unity Gateway: Governs enterprise AI with access control, cost control, observability and guardrails.
  • Lakehouse (Databricks SQL): Provides serverless data warehousing on open lake data with governance and AI built-in.
  • Agent Bricks: Used to build production-ready AI agents grounded in an organization's data.

Used for

  • Building and running apps, agents and AI on data
  • Building reliable ETL pipelines for batch and streaming
  • Creating dashboards using natural language
  • Governing compliance across data, models, dashboards and agents
  • Running SQL queries for BI reporting and analytics

Who it is for

  • Used by data and AI teams.
  • Used by business users.
  • Used by startups and global enterprises.
  • Serves data engineers, data scientists, analysts and AI teams.

From Databricks's own website, read 2026-10-23. How we build listings.

How to evaluate Data Warehouse & Lakehouse

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Databricks. The category covers two kinds of product, so both frameworks are here. Most buyers need one of them.

Cloud Data Warehouse
25%
Query Performance & Concurrency
TPC-DS benchmarks, concurrent user support, query queuing, automatic scaling, sub-second response for dashboards
20%
Data Ingestion & Integration
Streaming ingestion (Kafka, Kinesis), batch loading, CDC support, native connectors, data sharing/marketplace
20%
Governance & Security
Column/row-level security, dynamic data masking, data lineage, access policies, audit logging, compliance certifications
15%
AI/ML Integration
Native ML runtimes (Python/Spark), feature store, vector search, LLM integration, model serving
10%
Cost Management
Compute/storage separation, auto-suspend, resource monitors, usage attribution, reserved capacity pricing
10%
Ecosystem & Tooling
BI tool compatibility, dbt/Airflow integration, Iceberg/Delta support, partner ecosystem, marketplace
Data Lakehouse Platforms
25%
Open Format & Catalog Interoperability
Native read AND write to Apache Iceberg and/or Delta Lake, Iceberg REST Catalog support, credential vending for external engines, cross-format bridges (Delta UniForm, Apache XTable), and whether grants and lineage travel with the table when another engine reads it
20%
Query Engine Performance & Concurrency
Vectorized execution (Photon, Arrow/Gandiva, native engines), caching and materialization (reflections, result cache, Warp Speed), high-concurrency BI behavior, autoscaling, and predictable performance on your own data — not vendor benchmarks
20%
Data Engineering, Streaming & AI/ML
Batch and streaming ingestion into open tables, incremental/CDC and upsert support, orchestration, ML lifecycle (feature store, training, model serving), notebook and Python/Spark depth, and native LLM/agentic and vector capabilities
15%
Governance, Security & Lineage
A unified catalog spanning tables, files, ML models and (increasingly) unstructured data; fine-grained RBAC/ABAC, row/column masking, data sharing, automated lineage, and consistent policy enforcement across every engine that touches the data
10%
Operational Simplicity & Table Maintenance
Automated compaction, clustering, snapshot expiry and orphan-file cleanup; serverless vs. cluster sizing; multi-cloud and hybrid/on-prem reach; admin and FinOps tooling; and how much table toil the team must own versus the platform absorbing it
10%
Cost Model & Consumption Control
Consumption unit (DBU, credit, capacity unit, bytes/slots), separation of storage and compute, idle-suspend and autoscaling guardrails, egress and cross-region exposure, workload isolation, and the FinOps tooling to attribute and cap spend

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .

Quick Facts

www.databricks.com Pricing page
CategoryData & Analytics
SubcategoryData Warehouse & Lakehouse

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Keep reading

Buyer Guide
Cloud Data Warehouse
Job 03
Choose and buy technology

Every vendor now sells AI features, and each contract runs for years.

Job 04
Control technology spend

AI is billed per use, so its cost grows with adoption while the rest of the budget is under review.