Quary - Open-Source BI for Engineers

TL;DR

TL;DR: Quary is an open-source BI platform with a VSCode extension and Rust CLI that lets engineers define data sources, SQL models, and charts as version-controlled code, then deploy them straight to a data warehouse.

Source and Accuracy Notes

⚠️ This section is MANDATORY. All links must be verified from actual source, not guessed.

• Project page: quary.dev
• Source repository: github.com/quarylabs/quary — read README end-to-end
• License: Apache 2.0 (verified via GitHub API license.spdx_id)
• Y Combinator: W24 cohort (ycombinator.com/companies/quary)
• Latest release: v0.10.1 (tag verified via GitHub Releases API, 2026-01-23)
• GitHub stars: 2,374 (verified via GitHub REST API)

What Is Quary?

Quary is a business intelligence platform built specifically for engineers. Rather than clicking through a GUI to build ETL pipelines and dashboards, you work entirely in code — a VSCode extension handles the interface, and a Rust-based CLI handles compilation and execution.

The core loop: connect to your database, write SQL models in VSCode, test and version them like software, then deploy the results back to your data warehouse.

Quary supports PostgreSQL, Snowflake, BigQuery, Amazon Redshift, Supabase, DuckDB, and SQLite out of the box.

Setup Workflow

Step 1: Install the CLI

macOS / Linux via Homebrew:

brew install quarylabs/quary/quary

macOS / Linux via curl:

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

Pre-built binaries for other platforms are on the releases page.

Step 2: Install the VSCode Extension

The VSCode extension is available in the Visual Studio Marketplace. It depends on the CLI being installed first.

Step 3: Initialize a Project

mkdir example && cd example
quary init    # initialize a DuckDB demo project with sample data

Step 4: Compile and Build

quary compile # validate project structure and model references without a live database
quary build   # execute models and seeds against the target database
quary test    # run defined tests against the target database

Supported Databases

Quary connects to a wide range of SQL databases:

• Amazon Redshift
• Google BigQuery
• PostgreSQL
• Snowflake
• Supabase
• DuckDB
• SQLite

Core Concepts

Quary organizes analytics assets as code:

• Sources — define external data inputs: database tables, flat files, or APIs (via DuckDB)
• Models — SQL transformations that convert raw source data into analysis-ready datasets; support composability (one model can reference another)
• Charts — visual outputs defined in SQL
• Dashboards (in development) — combine multiple charts into a single view

All assets are stored as code files, making them naturally version-controllable, testable, and reviewable via pull request.

Practical Evaluation Checklist

• Does the tool work locally without a cloud account? Yes — DuckDB and SQLite require no external services
• Is the core workflow compatible with existing engineering practices? Yes — SQL-first, VSCode, version control, CI/CD testing
• Is the source code publicly available? Yes — github.com/quarylabs/quary
• Is there a well-maintained README with installation instructions? Yes — Homebrew and curl install paths documented
• Is the project actively maintained? Repository last pushed 2026-08-02; latest release v0.10.1 from January 2026

Security Notes

Quary compiles and executes SQL against your target database. The CLI and extension are open source, so you can audit the code before running it against production systems. As with any tool that executes SQL, use appropriate database permissions and review model SQL before running quary build against a production database.

FAQ

Q: What license is Quary published under? A: Apache 2.0, confirmed via the LICENSE file in the repository. The project is fully open source.

Q: How does Quary compare to dbt? A: Both use SQL-first transformations and treat models as version-controlled code. Quary adds a VSCode-native interface, a broader range of supported databases, and a built-in charting layer. dbt has a larger ecosystem and more integrations at this stage.

Q: Do I need a cloud service to use Quary? A: No. Quary ships with a DuckDB backend for local development that requires no external services. You can run quary init and experiment entirely offline.

Q: Is the VSCode extension free to use? A: Yes. Both the CLI and the VSCode extension are open source and free. There is no paid tier for the core product.

Conclusion

Quary brings software engineering discipline to business intelligence. By replacing the ETL GUI with a VSCode extension and SQL models, it lets engineering teams own the full analytics stack without learning a proprietary BI tool. The Apache 2.0 license, Rust-based CLI, and broad database support make it a practical open-source alternative for teams already comfortable at the SQL layer.

For a self-hosted BI tool that fits naturally into a developer workflow, Quary is worth evaluating.