Databricks documentation

The Databricks Lakehouse Platform, from the original creators of Apache Spark, enables data teams to collaborate in order to solve some of the world’s toughest problems.

Databricks excels at enabling data scientists, data engineers, and data analysts to work together on uses cases like:

• Applying advanced analytics for machine learning and graph processing at scale
• Using deep learning for harnessing the power of unstructured data such for AI, image interpretation, automatic translation, natural language processing, and more
• Making data warehousing fast, simple, and scalable
• Proactively detecting threats with data science and AI
• Analyzing high-velocity sensor and time-series IoT data in real-time
• Making GDPR data subject requests easy to execute

This documentation site provides how-to guidance and reference information for the Databricks Data Science & Engineering, Databricks Machine Learning, and Databricks SQL persona-based environments.

• Getting started

• Data Science & Engineering guide
  • Get started with Databricks Data Science & Engineering
  • Best practices
  • User guide
  • Data guide
  • Data engineering guide
  • Delta Lake and Delta Engine guide
  • Genomics guide
  • Administration guide
  • Databricks security guide
  • Developer tools and guidance
  • Languages
  • Databricks REST API reference
  • Release notes
• Machine Learning guide
  • Databricks Machine Learning overview
  • Tutorials
  • User guides
  • Feature guides

• Databricks SQL guide
  • Get started
  • User guide
  • Administration guide
  • Databricks SQL security guide
  • API reference
  • SQL reference
  • Release notes

• Integrations guide
  • Databricks Partner Connect guide
  • Databricks partners list
• Resources
  • Databricks datasets
  • Limits
  • Platform release process
  • Supported browsers
  • Support
  • Ideas Portal
  • Status Page