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Data Infrastructure

Databricks

Data intelligence platform unifying data engineering, warehousing and AI

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About Databricks

Databricks sells a lakehouse-based platform for data engineering, SQL analytics, machine learning and generative AI on cloud object storage. It created Apache Spark, Delta Lake and MLflow and offers Unity Catalog for governance. It added Lakebase, a managed Postgres service, after acquiring Neon in 2025.

Common use cases

  • Data engineering at scale
  • Enterprise BI on lakehouse
  • ML model training
  • Generative AI applications
  • Data sharing

Social listening

What people are saying about Databricks

Reviewers value Databricks's unified platform, scale and governance, and buyers typically plan for cost governance and onboarding.

What stands out

  • Reviewers value having data engineering, analytics and machine learning in one platform.
  • Governance and lineage controls are seen as strengths.
  • Assistant features such as Genie are viewed as helpful for querying and coding.
  • Broad integrations with cloud, BI and development tools are noted.

Things to consider

  • Worth setting up usage monitoring and budget controls early, especially with auto-scaling.
  • Teams new to the platform typically plan for onboarding and training time, including non-technical users.
  • Plan time to set up and maintain access policies and permissions.
  • Worth confirming support tiers and response times during evaluation.

Often compared with: Snowflake, Google BigQuery, Cloudera, Teradata, Dataiku

Sources (2)

Themes summarized from public reviews, community discussion and press coverage as of November 2026. They reflect public discussion, not a rating or an endorsement by AI Connect.

Products and services

Data Intelligence Platform

Lakehouse platform for data engineering, analytics and AI.

Lakebase

Serverless Postgres engine integrated with the lakehouse.

Agent Bricks

Developer platform for building AI agents.

Unity Catalog

Governance layer including metrics, glossary and domains.

Genie

Natural-language assistant for data and business questions.

Recent product updates

  • Genie One, an assistant for business teams working across structured and unstructured data, was announced at the 2026 summit (mid-2026).
  • Lakebase serverless Postgres gained branching, cross-region disaster recovery and hybrid vector and full-text search (2026).
  • Unity AI Gateway adds spend visibility and caps, routing and guardrails for models, agents and MCP services (2026).
  • The free tier was extended to include Genie Code, serverless GPUs, Lakebase and Agent Bricks (2026).

Key features

  • Lakehouse architecture
  • Spark processing
  • Unified governance
  • Serverless SQL
  • Model serving
  • Open table formats

Integrations

  • AWS
  • Microsoft Azure
  • Google Cloud
  • Fivetran
  • dbt
  • Tableau
  • Power BI
  • Apache Iceberg

Databricks alternatives

FAQs

How is it priced?

Usage-based, measured in compute units, with committed-spend discounts.

Which clouds does it run on?

AWS, Microsoft Azure and Google Cloud.

Is it open source?

It builds on open source projects such as Spark, Delta Lake and MLflow, but the platform is commercial.

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