# Databricks

> Leading Data and AI Platform for Enterprises

Databricks is a data and AI platform that lets organizations build analytics, applications, and AI agents on a unified, governed lakehouse.

Databricks is a data and AI company that provides a unified platform for data engineering, analytics, and machine learning. Data teams use it to process large datasets, build governed pipelines, and train and deploy models without maintaining separate systems for each workload. The platform serves organizations across many industries that need a consistent foundation for data and AI.

The platform is built on an open lakehouse architecture, which combines the structure and performance of a data warehouse with the flexibility and scale of a data lake. This removes the need to duplicate data across separate analytical and operational systems while maintaining consistent security and governance. Users can run SQL analytics, serve machine learning predictions, and ground AI agents in enterprise data through a shared catalog and unified access controls.

Its portfolio spans data engineering pipelines, a SQL-based data warehouse, a governance layer for data and AI assets, and tools for building and serving AI agents. It is used by data engineers, analysts, and AI teams at companies across finance, retail, healthcare, and other sectors to consolidate previously fragmented data and AI infrastructure into a single governed environment.

- Type: Company
- Sector: Data & Analytics
- Status: Active
- Founded: 2013-01-01
- Profile: https://thegrid.id/profiles/databricks

## Products

- [Databricks Data Intelligence Platform](https://thegrid.id/profiles/databricks): Databricks Data Intelligence Platform is a unified lakehouse platform combining data engineering, analytics, machine learning, and generative AI for enterprise data teams. The platform ingests and transforms data through automated pipelines, supports serverless data warehousing through an optimized query engine, and provides model training, serving, and retrieval infrastructure for building AI agents and applications. A governance layer applies unified access control, lineage tracking, and auditing across all data and AI assets, while built-in sharing capabilities allow secure data exchange across organizations. The platform is deployed across multiple major cloud providers and includes collaborative development environments, a machine learning lifecycle management system, and privacy-preserving data collaboration tools. (Developer Tooling, Live, Main Product)
- [Lakebase](https://thegrid.id/profiles/databricks): Lakebase is a serverless Postgres database integrated with the Databricks Data Intelligence Platform, built for transactional workloads behind data applications and AI agents. The database separates compute from storage, allowing autoscaling with scale-to-zero, instant branching for isolated development environments, and point-in-time recovery, while synchronizing with analytics tables in the lakehouse. It supports standard Postgres extensions, exposes an API for application access, and registers as a governed object within the platform's unified catalog. (Live)
- [Agent Bricks](https://thegrid.id/profiles/databricks): Agent Bricks is an AI agent platform on Databricks for building, deploying, and governing agents that operate on enterprise business data. It unifies model access, execution, governance, and context across a single system, letting users configure agents for tasks such as knowledge retrieval, document processing, and multi-agent coordination within a workspace. The platform applies automated evaluation and tuning to improve agent accuracy against custom metrics, integrates with a model operations layer for tracing, and connects to retrieval infrastructure for grounding agent responses in enterprise data. Each configured agent is hosted at an endpoint that applications can call directly. (Live)
- [Lakewatch](https://thegrid.id/profiles/databricks): Lakewatch is an agentic security information and event management platform built on the Databricks Data Intelligence Platform for enterprise security operations. It unifies security telemetry, IT logs, and business data under a single governance layer to support large-scale threat hunting and AI-driven defense. Security logs are ingested through automated pipelines, and AI agents triage, investigate, and respond to threats at machine speed, with governance and access control applied across all security data through a unified catalog layer. (Live)

## Links

- [Main](https://databricks.com)
- [Blog](https://www.databricks.com/blog)
- [Documentation](https://docs.databricks.com)
- [Privacy policy](https://www.databricks.com/legal/privacynotice)
- [Terms of Service](https://www.databricks.com/legal/terms-of-use)
- [Media Kit/branding](https://brand.databricks.com)
- [LinkedIn](https://www.linkedin.com/company/databricks)
- [Facebook](https://www.facebook.com/databricksinc)
- [YouTube](https://www.youtube.com/@Databricks)
- [Twitter / X](https://x.com/databricks)

## Legal

This data is provided under two licensing models.

Model 1 - open data. The open data core is licensed under the Open Database License (ODbL) — a copyleft/share-alike license allowing free use, modification, and sharing for any purpose. Attribution is required: include "Powered by The Grid" linking to https://thegrid.id wherever the data is used. Derivative databases must be shared under ODbL terms. Trademarks, logos, and brand names (marked "Special TPIP") are not covered by the ODbL. For ML/AI use: training datasets that substantially extract ODbL data are Derivative Databases and must be shared under ODbL if publicly used; models must be attributed in documentation.

Model 2 - commercial licenses. The Grid Data Service (TGDS) is also available as a commercial license. See [Web Services Terms](https://about.thegrid.id/legal/web-services-terms) for commercial use that is not covered by the open data service.

The following applies to both licensing models.
- All data is provided AS IS with no guarantees of accuracy.
- The Grid does not provide investment, legal, or tax advice, and
- a project's presence in The Grid's data does not imply endorsement.


- [Data Licensing & Disclaimers](https://about.thegrid.id/legal/data-licensing-and-disclaimers): Licensing models and disclaimers
- [Open Data (ODbL)](https://about.thegrid.id/legal/open-data): Open Database License terms and scope for The Grid's open data service.
- [Attribution Guidelines](https://about.thegrid.id/legal/open-data-attribution-guidelines): How to attribute The Grid's data when using the open data service.
- [Web Services Terms](https://about.thegrid.id/legal/web-services-terms): commercial Terms for using data services.
- [All Legal Documents](https://about.thegrid.id/legal): Full index of terms, policies, and disclosures

## Optional

- [TGS Field Descriptions](https://github.com/The-Grid-Data/workspace-public/blob/main/references/tgs-field-descriptions.md): Schema field definitions for The Grid's data model
- [TGS JSON Structure](https://github.com/The-Grid-Data/workspace-public/blob/main/references/tgs-json-structure.md): Data model structure and relationships
- [Product Type Definitions](https://github.com/The-Grid-Data/workspace-public/blob/main/references/product-type-definitions.md): Definitions for each product type
- [Product Type Classification](https://github.com/The-Grid-Data/workspace-public/blob/main/references/product-type-classification.md): Guide for classifying products into types
- [Grid MCP Guide](https://github.com/The-Grid-Data/workspace-public/blob/main/grid-search/skills/grid-query/references/grid-mcp-guide.md): How to query The Grid programmatically via MCP