Discover, Analyze, and Collaborate

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

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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.

Visualizations

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.

Templating

Write dynamically generated queries via Jinja2 templating. Set variables in DataDoc on the fly.

Scheduling

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.

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.

Exporter

Upload query results from Querybook to other tools for further analyses.

Notification

Get notified upon completion of queries and DataDoc invitations via IM or email.

Result Transform

Augment query results to provide meaningful statistics and visualizations.

Check out our documentation to see all customization options.

Integrations

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

Deployment

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.

Feedback

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

Interested?

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

Visit Github to fork/clone the repo.

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

Discover, Analyze, and Collaborate

Step 1: Download

Step 2: Run