Workspace user guide

This guide provides information about the tools available to you in the Databricks workspace, as well as migration and security guidance.

• Databricks runtimes
  • Databricks Runtime
  • Databricks Runtime for Machine Learning
  • Databricks Runtime for Genomics
  • Databricks Light
• Workspace
  • Workspace assets
  • Work with workspace objects
  • Get workspace, cluster, notebook, model, and job identifiers
• Clusters
  • Create a cluster
  • Manage clusters
  • Configure clusters
  • Task preemption
  • Customize containers with Databricks Container Services
  • Cluster node initialization scripts
  • GPU-enabled clusters
  • Single Node clusters
  • Pools
  • Web terminal
• Notebooks
  • Manage notebooks
  • Use notebooks
  • Visualizations
  • Dashboards
  • Widgets
  • Notebook workflows
  • Package cells
• Jobs
  • View jobs
  • Create a job
  • View job details
  • Run a job
  • Run a job with different parameters
  • Notebook job tips
  • JAR job tips
  • View job run details
  • Export job run results
  • Edit a job
  • Delete a job
  • Library dependencies
  • Advanced job options
  • Control access to jobs
• Libraries
  • Workspace libraries
  • Cluster libraries
  • Notebook-scoped Python libraries
• Databricks File System (DBFS)
  • DBFS root
  • Browse DBFS using the UI
  • Mount object storage to DBFS
  • Access DBFS
• Developer tools
  • Databricks Utilities
  • Databricks CLI
  • Databricks Connect
  • Continuous integration and delivery
  • Managing dependencies in data pipelines
• Migration
  • Migrate production workloads to Databricks
  • Migrate single node workloads to Databricks
• Security and privacy
  • Access control
  • Secret management
  • Credential passthrough
  • Credential redaction
  • Customer managed keys for notebooks
  • Secure cluster connectivity
  • Encrypt traffic between cluster worker nodes
  • IP access lists
  • Configure domain name firewall rules
  • Best practices: GDPR and CCPA compliance using Delta Lake
  • HIPAA-compliant deployment
  • Best practices: Data governance on Databricks