- ๐ Connect to their Database
- ๐ Write SQL queries to transform, organize, and document tables in a database
- ๐ Create charts, dashboards and reports (in development)
- ๐งช Test, collaborate & refactor iteratively through version control
- ๐ Deploy the organised, documented model back up to the database
View the documentation.
Define and manage the following asset types as code:
- Sources: Define the external data sources that feed into Quary, such as database tables, flat files, or APIs (with DuckDB).
- Models: Transform raw data from sources into analysis-ready datasets using SQL, this lets engineers split complex queries into atomic components.
- Charts: Create visual representations of your data using SQL.
- ๐ง Dashboards (WIP): Combine multiple charts into a single view, allowing engineers to monitor and analyze data in one place.
- ๐ง Reports (WIP): Create detailed reports to share insights and findings with your team or stakeholders.
Quary is a VSCode Extension (Interface) & Rust-based CLI (Core)
The VSCode extension can be installed here. Note that it depends on the CLI being installed.
brew install quarylabs/quary/quary
Quary can be installed using curl on Linux/Mac using the following command:
curl -fsSL https://raw.githubusercontent.com/quarylabs/quary/main/install.sh | bash
Other builds are available in the releases page to download.
Once installed, a sample project can be created and run as follows:
mkdir example # create an empty project folder
cd example
quary init # initialize DuckDB demo project with sample data
quary compile # validate the project structure and model references without database
quary build # build and execute the model views/seeds against target database
quary test -s # run defined tests against target database
Join our Slack channel, for help, ideas, and discussions.
If you run into any problems using Quary, please let us know. We want Quary to be easy-to-use, so if you are getting confused, it is our fault, not yours. Create an issue and we'll be happy to help you out.
SQRUFF, a compact, high-speed SQL linter, engineered with Rust efficiency.