whale is a lightweight data discovery, documentation, and quality engine for your data warehouse.
- Automatically index all of the tables in your warehouse as plain markdown files -- so they're easily versionable, searchable, and editable either locally or through a remote git server.
- Search for tables and documentation through the CLI or through a git remote server like Github.
- Define and schedule basic metric calculations (in beta).
- Run quality tests and systematically monitor anomalies (in roadmap).
For a live demo, check out dataframehq/whale-bigquery-public-data.
brew install dataframehq/tap/whale
Make sure rust is installed on your local system. Then, clone this directory and run the following in the base directory of the repo:
make && make install
If you are running this multiple times, make sure
~/.whale/libexec does not exist, or your virtual environment may not rebuild. We don't explicitly add an alias for the
whale binary, so you'll want to add the following alias to your
For individual use, run the following command to go through the onboarding process. It will (a) set up all necessary files in
~/.whale, (b) walk you through cron job scheduling to periodically scrape metadata, and (c) set up a warehouse:
The cron job will run as you schedule it (by default, every 6 hours). If you're feeling impatient, you can also manually run
wh etl to pull down the latest data from your warehouse.
For team use, see the docs for instructions on how to set up and point your whale installation at a remote git server.
Seeding some sample data
If you just want to get a feel for how whale works, remove the
~/.whale directory and follow the instructions at dataframehq/whale-bigquery-public-data.
Go go go!
to search over all metadata. Hitting
enter will open the editable part of the docs in your default text editor, defined by the environmental variable
$EDITOR (if no value is specified, whale will use the command