Data Access: Quickly access available data sets or connect to any data sources, on-premises or in the cloud.
Multi-Languages Support: Explore data using interactive notebooks with support for multiple programming languages within the same notebook, including R, Python, Scala, and SQL.
Interactive Visualizations: Visualize insights through a wide assortment of point-and-click visualizations. Or use powerful scriptable options like matplotlib, ggplot, and D3.
Real-Time Coauthoring: Work on the same notebook in real-time while tracking changes with detailed revision history.
Comments: Leave a comment and notify colleagues from within shared notebooks.
Automatic Versioning: Tracking changes and versioning automatically happens so that you can continue where you left off or revert changes.
Git Versioning in Git: Integrate with Git for resilient and advanced versioning capabilities.
Runs Sidebar: Automatically log experiments, parameters and results from notebooks directly to MLflow as runs, and quickly see and load previous runs and code versions from the sidebar.
Dashboards: Share insights with your colleagues and customers, or let them run interactive queries with Spark-powered dashboards.
Run Notebooks as Jobs: Turn notebooks or JARs into resilient production jobs with a click or an API call.
Jobs Scheduler: Execute jobs for production pipelines on a specific schedule.
Notebook Workflows: Create multi-stage pipelines with the control structures of the source programming language.
Notifications and Logs: Set up alerts and quickly access audit logs for easy monitoring and troubleshooting.
Permissions Management: Quickly manage access to each individual notebook, or a collection of notebooks, and experiments, with one common security model.
Clusters: Quickly attach notebooks to auto-managed clusters to efficiently and cost-effectively scale up compute at unprecedented scale.
Integrations: Connect to Tableau, Looker, PowerBI, RStudio, SnowFlake etc allowing data scientists and engineers to use familiar tools.
