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Databricks Notebooks

Collaborative data science with familiar languages and tools

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Work across engineering, data science and machine learning teams in one workspace. Use multiple languages, built-in data visualizations and automatic versioning, all within Notebooks.

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Work together

Share Notebooks and work with peers across teams in multiple languages (R, Python, SQL and Scala) and libraries of your choice. Real-time coauthoring, commenting and automated versioning simplify collaboration while providing control.


Share insights

Quickly discover new insights with built-in interactive visualizations, or leverage libraries such as Matplotlib and ggplot. Export results and Notebooks in HTML or IPYNB format, or build and share dashboards that always stay up to date.


Production at scale

Schedule Notebooks to automatically run machine learning and data pipelines at scale. Create multistage pipelines using Databricks Workflows. Set up alerts and quickly access audit logs for easy monitoring and troubleshooting.

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Databricks Assistant

Databricks Assistant is your context-aware AI assistant, available natively in Databricks Notebooks, SQL editor and file editor. Databricks Assistant lets you query data through a conversational interface, making you more productive inside Databricks. Describe your task in English and let the Assistant generate SQL queries, explain complex code and automatically fix errors. The Assistant leverages Unity Catalog metadata to understand your tables, columns, descriptions and popular data assets across your company to provide responses that are personalized to you.


Data Access

Quickly access available data sets or connect to any data source, on-premises or in the cloud.


Share insights with your colleagues and customers, or let them run interactive queries with Spark-powered dashboards.

Multi-language support

Explore data using interactive notebooks with support for multiple programming languages within the same notebook, including R, Python, Scala and SQL.

Run Notebooks as Jobs

Turn notebooks or JARs into resilient production jobs with a click or an API call.

Interactive Visualizations

Visualize insights through a wide assortment of point-and-click visualizations. Or use powerful scriptable options like Matplotlib, ggplot and D3.

Jobs Scheduler

Execute jobs for production pipelines on a specific schedule.

Real-Time Coauthoring

Work on the same notebook in real time while tracking changes with detailed revision history.

Notebook Workflows

Create multistage pipelines with the control structures of the source programming language.


Leave a comment and notify colleagues from within shared Notebooks.

Notifications and Logs

Set up alerts and quickly access audit logs for easy monitoring and troubleshooting.

Automatic Versioning

Automatic change-tracking and versioning to help you pick up where you left off.

Permissions Management

Quickly manage access to each individual notebook, or a collection of Notebooks, and experiments, with one common security model.

Git-based Repos

Simplified Git-based collaboration, reproducibility and CI/CD workflows.


Quickly attach Notebooks to auto-managed clusters to efficiently and cost-effectively scale up compute.

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.


Connect to Tableau, Looker, Power BI, RStudio, Snowflake and also through your favorite IDEs such as VS Code — allowing data scientists and engineers to use their tools of choice.

How it works

Shared and interactive Notebooks, experiments and extended files support allow data scientist teams to organize, share and manage complex data science projects more effectively throughout the lifecycle. APIs and Job Scheduler allow data engineering teams to quickly automate complex pipelines, while business analysts can directly access results via interactive dashboards.


Ready to get started?