Quary serves as an efficient tool for engineers, connecting their databases and enabling them to write SQL queries to transform, organize, and document their tables. In this article, we guide you on how to get started with Quary, from installation to usage. Let’s dive in!

What Can You Do with Quary?

With Quary, engineers can:

• Connect to their database
• Write SQL queries to transform, organize, and document tables in a database
• Create charts, dashboards, and reports (still in development)
• Test, collaborate, and refactor iteratively through version control
• Deploy the organized, documented model back to the database

For more information on these features, you can view the documentation.

Supported Databases

Quary supports several databases, including:

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Asset Types in Quary

Quary allows you to define and manage various asset types as code, including:

• Sources: Define external data sources that feed into Quary, such as database tables, flat files, or APIs.
• Models: Transform raw data from sources into analysis-ready datasets using SQL, making complex queries simpler.
• Charts: Create visual representations of your data using SQL.
• Dashboards: Combine multiple charts into a single view (currently in progress).
• Reports: Generate detailed reports to share insights with team members or stakeholders (currently in progress).

Getting Started with Quary

Installation

Quary can be installed as a VSCode extension along with a Rust-based CLI. Here’s how:

Extension

The VSCode extension can be installed here. Remember, it relies on the CLI being installed first.

CLI Installation

For macOS, you can install Quary using Homebrew:

brew install quarylabs/quary/quary

If you’re using Linux or macOS, use curl to install:

curl -fsSL https://raw.githubusercontent.com/quarylabs/quary/main/install.sh | bash

Check other builds on the releases page.

Usage

Once installed, create and run a sample project 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

Troubleshooting Tips

If you encounter any issues while using Quary, keep these tips in mind:

• Ensure that all dependencies are correctly installed, including the required CLI.
• Check for updates or issues on the GitHub issues page.
• Join our Slack channel for help, ideas, and discussions here.
• If confusion persists, remember: it’s not your fault! Please report issues directly so we can assist you.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.