Open-source Coalesce alternatives

dbt is a framework for transforming data in a warehouse using the same practices that software engineers apply to applications. Analysts and analytics engineers write models, and dbt parses, compiles and runs the project so that transformations are version-controlled and repeatable.

The repository now holds the Apache 2.0 source of dbt v2.0, a ground-up rewrite in Rust. According to its README, v2.0 parses and compiles projects much faster than v1, enforces a stricter language specification at parse time, produces Parquet artifacts alongside the existing JSON ones, and ships as one standalone executable, so no Python installation is needed. The local documentation experience was rebuilt around those artifacts to handle large projects.

The Apache 2.0 code is available to everyone, while the packaged dbt distribution adds dbt-specific customizations under a separate dbt product license. v2.0 supports macOS and Linux on x86-64 and ARM. The v1 Python implementation continues on the 1.latest branch. dbt Labs also offers dbt as a commercial cloud platform.

Quary is an open-source business intelligence tool aimed at engineers. It connects to a database, lets you write SQL to transform, organize, and document tables, and treats the resulting assets as code that can be tested, reviewed, and versioned. It is written in Rust and released under the Apache-2.0 license.

Assets include sources, which define external data such as database tables, flat files, or APIs through DuckDB, and models, which turn raw data into analysis-ready datasets with SQL so complex logic can be split into smaller steps. The workflow is to test, collaborate, and refactor iteratively through version control, then deploy the organized and documented model back to the database. Charts, dashboards, and reports are listed as in development.

Its topics mention data modeling, ELT, and big data, and the documentation lists supported databases. Quary suits data and analytics engineers who like version-controlled, code-first workflows similar to dbt and want an integrated path from modeling to reporting.

Bruin is a data pipeline tool that brings data ingestion, transformation and data quality into a single framework. Instead of stitching together separate tools for loading, modeling and testing, you describe a pipeline with SQL, Python or R assets and run it against the major data platforms, such as BigQuery and Snowflake.

Pipelines can ingest data from different sources, materialize tables and views, and build incremental tables. Python assets run in isolated environments using uv, Jinja templating cuts down on repetition, and built-in quality checks validate results. A dry-run mode validates a pipeline end to end, secrets are injected through environment variables, and a VS Code extension supports day-to-day development.

Bruin is written in Go, licensed under Apache-2.0 and distributed as a command-line tool that is easy to install. It runs on your local machine, an EC2 instance or GitHub Actions, so no dedicated orchestration server is required. It suits data engineers and analytics teams who want pipelines defined as code in a repository.