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DiscoverAnalyze, and Collaborate

Querybook is Pinterest’s open-source big data IDE via a notebook interface.

Used by Engineers and Data Scientists from

Key Features
Querying done right
Querybook’s core focus is to make composing queries, creating analyses, and collaborating with others as simple as possible
Collaborative DataDoc
Organize rich text, queries, and charts into a notebook to easily document your analyses. Work collaboratively with others in a DataDoc and get real-time updates.
Smart Query Editor
The Query Editor is aware of your tables and their columns, as such it provides autocompletion, syntax highlighting, and the ability to hover or click on a table to view its information.
No need to leave Querybook to create charts to quickly visualize your results. With a familiar interface easily create line, bar, stacked area, pie, horizontal bar, donut, scatter, and table charts. Add them then to your DataDoc to complete your data narrative.
Write dynamically generated queries via Jinja2 templating. Set variables in DataDoc on the fly.
Built-in scheduling functionality allows automatic DataDoc updates on set intervals. Combined with exporting, Querybook can send scheduled updates to external apps.
Query Analytics
Querybook auto analyzes executed queries to provide data lineage, example queries, frequent user information, search/auto-completion ranking.
Collaborative DataDoc
Check out our documentation to learn more about what Querybook can offer.
Plugin Features
Suit your needs
Have a different tech stack? No problem. Every aspect of Querybook can be dynamically configured via the plugin system to let you fully leverage all of its features.
Query Engine
Supply your own query engine and add actionable error messages, useful metadata, and additional security measures.
Upload query results from Querybook to other tools for further analyses.
Get notified upon completion of queries and DataDoc invitations via IM or email.
Result Transform
Augment query results to provide meaningful statistics and visualizations.
Query Engine
Check out our documentation to see all customization options.
Out-of-the-box support
With the help of plugins, additional integrations can be added easily.

The following Databases are supported


View the full list of supported databases here.

Querybook also supports the following Cloud Platforms

Setup & deploy in minutes.

Querybook is configured to work entirely inside Docker. With the help of docker-compose, you can start a full-featured demo instance within minutes.

When deploying to production, Querybook comes with an example K8s file for you to easily deploy to K8s cluster. You can also use docker-compose run to deploy each individual service to separate machines.

Here’s what users are saying about Querybook

Querybook has been instrumental to the Advertiser Growth Team at Pinterest. It allows us to opportunity size new experiment ideas and do offline experiment analysis in a collaborative and scalable way.

Arvin Rezvanpour, SWE @Pinterest

I rely on Querybook every day to query, organize and analyze Pinterest data. It's a fast and intuitive program that gets out of the way and allows me to focus on identifying trends and opportunities.

Jesse Lumarie, SWE @Pinterest

I know I am so incredibly late to the party, but I made my first DataDoc yesterday and I think I’m in love…

OJ Bright, Quantitative Researcher @GrandRounds

Try it out
Use the following resources to get a demo instance running in a few minutes.

Step 1: Download

Visit Github to fork/clone the repo.

Step 2: Run

Run make in the root directory to start the demo instance.