Databricks Documentation llms.txt · AI Docs Browser · mdream [llms.txt Tools](https://mdream.dev/llms-txt/databricks/llms-txt) # Databricks Documentation llms.txt Browse Databricks Documentation documentation as markdown. Click any page in the sidebar to view it, or copy the full llms.txt for your AI tools. docs.databricks.com/llms.txt [Raw llms.txt](https://docs.databricks.com/llms.txt) [Raw llms-full.txt](https://docs.databricks.com/llms-full.txt) Copy Markdown [](https://docs.databricks.com/llms.txt) llms.txt Overview and getting started Main documentation index What is Databricks? Databricks components Get started tutorials on Databricks Query and visualize data Import and visualize CSV data from a notebook Create a table Build an ETL pipeline (Lakeflow pipelines) Build an ETL pipeline (Apache Spark) Train and deploy an ML model Query LLMs and prototype gen AI agents Get started as a business analyst with Genie Customer segmentation with Genie Code Free trial setup Free edition Load and transform data using Spark DataFrames Core platform Data lakehouse Delta Lake Unity Catalog Database objects Catalogs Volumes Schemas Lakebase Postgres Workspace navigation Search for workspace objects Catalog Explorer Genie Genie One Genie Agents Genie Code Use Genie on mobile Databricks AI assistive features Notebooks Notebook widgets Compute resources Compute configuration reference Instance pools GPU compute Serverless compute Photon engine Files What are workspace files? Libraries Databricks Runtime Spark overview Data sources and formats Data guides Data sources overview Tables Table types Change Data Capture (CDC) Apache Iceberg Managed tables External tables Transactions Model semi-structured data OpenSharing External access Parquet CSV JSON Avro ORC XML Binary files Discover data Sample datasets Data engineering Data engineering overview Lakeflow pipelines Lakeflow pipelines concepts Pipeline developer reference Monitor pipelines Lakeflow pipelines tutorials Lakeflow Connect Standard connectors Managed connectors Structured streaming Run your first Structured Streaming workload Lakeflow Jobs Job tasks Job scheduling Lakeflow Designer Ingest data from cloud object storage Auto Loader Lakeflow Connect SaaS connectors Lakeflow Connect database connectors Managed file source connectors Managed streaming connectors Machine learning and AI AI and machine learning overview Concepts: Data science and machine learning on Databricks Concepts: Generative AI on Databricks Build AI agents Agent Framework Connect agents to tools Omnigent Foundation model APIs AI playground AI Runtime (serverless GPU) AI Runtime CLI MLflow for GenAI MLflow on Databricks Manage models in Unity Catalog Model Serving Supported foundation models on Model Serving AutoML Feature Store AI Search Deep learning Distributed training Hyperparameter tuning AI governance with Unity AI Gateway AI governance guide Model Context Protocol (MCP) on Databricks MLflow Tracing Evaluate and monitor AI agents MLflow Prompt Registry Foundation Model Fine-tuning Batch model inference Ray on Databricks External models in Model Serving SQL and analytics Data warehousing Get started with data warehousing using Databricks SQL SQL warehouses Monitor a SQL warehouse Serverless SQL warehouses Queries Query history SQL editor Business semantics Dashboards and visualizations Data modeling in AI/BI dashboards Embed a dashboard Dashboard tutorials Alerts AI/BI AI/BI administration guide Query data Visualizations Data governance and security Security overview Data governance Unity Catalog overview Unity Catalog setup guide Get started with Unity Catalog Access control Configure SSO Access control in Unity Catalog Unity Catalog privileges Row and column filters Views Unity Catalog connections Data lineage Audit logging Compliance Encryption Network security Data exfiltration protection Customer-managed keys Secret management Attribute-based access control Service policies for AI securables Governed tags Data quality monitoring Specialized features Partner connect Marketplace Connect to external sources Clean rooms Share data and AI assets securely OpenSharing Administration Administration overview User and group management SCIM provisioning Automatic identity management Service principals Personal access tokens Workspace settings Account settings System tables Cost management Create a workspace Create and manage compute policies Developer tools Develop on Databricks Local development tools CI/CD on Databricks Enable workload identity federation in CI/CD REST API Use the Genie Agents API SDK for Python SDK for Java SDK for Go SDK for JavaScript SDK for R Databricks CLI Databricks Utilities Git integration GitHub Actions Databricks Apps Declarative Automation Bundles Terraform provider Databricks Connect Visual Studio Code (or Cursor) extension SSH tunnel JDBC driver ODBC driver User-defined functions (UDFs) Python UDFs Scala UDFs Authorize access to Databricks resources Reference and language-specific guides Reference overview REST API reference Machine readable copy of the REST API reference SQL reference SQL functions SQL data types CLI reference PySpark reference Choose a development language Python on Databricks PySpark on Databricks Scala on Databricks R on Databricks Troubleshooting and support Error classes Troubleshoot compute issues Resources Support Status page Migration and best practices Migration guides Migrate from Apache Spark Migrate to Unity Catalog Best practices Data engineering best practices Developer best practices Optimizations Integrations and connectors Technology partners Tableau Power BI Fivetran Apache Kafka Apache Airflow dbt integration Microsoft Excel Google Sheets Microsoft Power Platform Additional resources Agent skills for AI coding assistants Release notes Supported regions Resource limits Pricing Training and certification Community forums Knowledge base Glossary ## What is Databricks Documentation's llms.txt? Databricks Documentation publishes an `/llms.txt` file that provides AI systems with a structured index of its documentation. It follows the [llms.txt specification](https://llmstxt.org), organizing links to guides, API references, and tutorials under section headings so that LLMs like ChatGPT, Claude, and Gemini can quickly understand what Databricks Documentation offers and where to find details. Pass Spec 15 Sections 251 Links 45.8 KB Size ~11.7k Tokens Sections: Overview and getting started · Core platform · Data sources and formats · Data engineering · Machine learning and AI · SQL and analytics · and 9 more ## Add Databricks Documentation Docs to Your AI Assistant Select your tool to see how to add Databricks Documentation's llms.txt as a documentation source. Cursor Windsurf Claude Code ChatGPT Zed Copilot 1. 1 Open any chat or composer panel 2. 2 Type @Docs and select "Add new doc" 3. 3 Paste the URL: https://docs.databricks.com/llms.txt 4. 4 Wait for the green dot (indexing complete) 5. 5 Reference @Docs in chat when asking about Databricks Documentation Cursor crawls the URL and indexes all subpages. Add a trailing slash to index child pages too. ## Spec Compliance 2 notes ## Frequently Asked Questions Where is Databricks Documentation's llms.txt file? Databricks Documentation publishes its llms.txt at https://docs.databricks.com/llms.txt. This file provides a structured, markdown-formatted index of Databricks Documentation's documentation that AI systems can consume to understand the project's APIs, guides, and references. How do I use Databricks Documentation's llms.txt with AI coding assistants? Copy the llms.txt content and paste it into your AI assistant (Cursor, Windsurf, Claude, ChatGPT) as context. This gives the AI an accurate map of Databricks Documentation's documentation so it can provide better code suggestions and answers about Databricks Documentation. What does Databricks Documentation's llms.txt contain? Databricks Documentation's llms.txt contains 15 sections and 251 documentation links in 45.8 KB (~11.7k tokens). Key sections include Overview and getting started, Core platform, Data sources and formats, Data engineering, Machine learning and AI, SQL and analytics. How many tokens does Databricks Documentation's llms.txt use? The concise llms.txt index is approximately 11.7k tokens (45.8 KB). Most AI assistants can fit this within their context window. For the full expanded documentation, look for an llms-full.txt variant which embeds all content inline. What is Databricks Documentation? Comprehensive documentation for the Databricks Data Intelligence Platform, including guides for data engineering, machine learning, AI, analytics, governance, and administration across all supported cloud platforms.. Databricks Documentation's llms.txt file helps AI systems understand this by providing a structured overview of its documentation, making it easier for developers to get accurate AI-assisted help when working with Databricks Documentation. Can I generate an llms.txt for my own project? Yes. Use the free llms.txt generator at mdream.dev/tools/llms-txt/generator to crawl your site and produce a spec-compliant llms.txt file. You can also validate existing files with the llms.txt validator. ## Browse More llms.txt Files [MDBootstrap Important! A new UI Kit version for Bootstrap 5 is available. 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