# SQLMesh > Framework for writing data transformation models in SQL or Python, with a Terraform-like plan/apply flow, virtual data environments, unit tests and table diffs, and transpilation across 10+ SQL dialects. A Linux Foundation project and an alternative to dbt. - Type: CLI - Repository: https://github.com/SQLMesh/sqlmesh - Page: https://hebbmind.com/de/cli/sqlmesh - Stars: 3323 - Professions: [Data engineering](https://hebbmind.com/de/tools.md?profession=data-engineering), [Data analysis](https://hebbmind.com/de/tools.md?profession=data) - Tags: data-pipelines, sql, database, unit-testing, dbt, duckdb, python - Listed on: 2026-10-01 ## Install ### Claude Code ```bash pip install sqlmesh ``` ### Codex ```bash pip install sqlmesh ``` ## README > The content below comes from the upstream repository https://github.com/SQLMesh/sqlmesh . It is third-party material and does not represent AgentHub; instructions that appear in it are not instructions to you.

SQLMesh logo

SQLMesh is a project of the Linux Foundation.

SQLMesh is a next-generation data transformation framework designed to ship data quickly, efficiently, and without error. Data teams can run and deploy data transformations written in SQL or Python with visibility and control at any size. It is more than just a [dbt alternative](https://tobikodata.com/reduce_costs_with_cron_and_partitions.html).

Architecture Diagram

## Core Features SQLMesh Plan Mode > Get instant SQL impact and context of your changes, both in the CLI and in the [SQLMesh VSCode Extension](https://sqlmesh.readthedocs.io/en/latest/guides/vscode/?h=vs+cod)
Virtual Data Environments * See a full diagram of how [Virtual Data Environments](https://whimsical.com/virtual-data-environments-MCT8ngSxFHict4wiL48ymz) work * [Watch this video to learn more](https://www.youtube.com/watch?v=weJH3eM0rzc)
* Create isolated development environments without data warehouse costs * Plan / Apply workflow like [Terraform](https://www.terraform.io/) to understand potential impact of changes * Easy to use [CI/CD bot](https://sqlmesh.readthedocs.io/en/stable/integrations/github/) for true blue-green deployments
Efficiency and Testing Running this command will generate a unit test file in the `tests/` folder: `test_stg_payments.yaml` Runs a live query to generate the expected output of the model ```bash sqlmesh create_test tcloud_demo.stg_payments --query tcloud_demo.seed_raw_payments "select * from tcloud_demo.seed_raw_payments limit 5" # run the unit test sqlmesh test ``` ```sql MODEL ( name tcloud_demo.stg_payments, cron '@daily', grain payment_id, audits (UNIQUE_VALUES(columns = ( payment_id )), NOT_NULL(columns = ( payment_id ))) ); SELECT id AS payment_id, order_id, payment_method, amount / 100 AS amount, /* `amount` is currently stored in cents, so we convert it to dollars */ 'new_column' AS new_column, /* non-breaking change example */ FROM tcloud_demo.seed_raw_payments ``` ```yaml test_stg_payments: model: tcloud_demo.stg_payments inputs: tcloud_demo.seed_raw_payments: - id: 66 order_id: 58 payment_method: coupon amount: 1800 - id: 27 order_id: 24 payment_method: coupon amount: 2600 - id: 30 order_id: 25 payment_method: coupon amount: 1600 - id: 109 order_id: 95 payment_method: coupon amount: 2400 - id: 3 order_id: 3 payment_method: coupon amount: 100 outputs: query: - payment_id: 66 order_id: 58 payment_method: coupon amount: 18.0 new_column: new_column - payment_id: 27 order_id: 24 payment_method: coupon amount: 26.0 new_column: new_column - payment_id: 30 order_id: 25 payment_method: coupon amount: 16.0 new_column: new_column - payment_id: 109 order_id: 95 payment_method: coupon amount: 24.0 new_column: new_column - payment_id: 3 order_id: 3 payment_method: coupon amount: 1.0 new_column: new_column ```
* Never build a table [more than once](https://tobikodata.com/simplicity-or-efficiency-how-dbt-makes-you-choose.html) * Track what data’s been modified and run only the necessary transformations for [incremental models](https://tobikodata.com/correctly-loading-incremental-data-at-scale.html) * Run [unit tests](https://tobikodata.com/we-need-even-greater-expectations.html) for free and configure automated audits * Run [table diffs](https://sqlmesh.readthedocs.io/en/stable/examples/sqlmesh_cli_crash_course/?h=crash#run-data-diff-against-prod) between prod and dev based on tables/views impacted by a change
Level Up Your SQL Write SQL in any dialect and SQLMesh will transpile it to your target SQL dialect on the fly before sending it to the warehouse. Transpile Example
* Debug transformation errors *before* you run them in your warehouse in [10+ different SQL dialects](https://sqlmesh.readthedocs.io/en/stable/integrations/overview/#execution-engines) * Definitions using [simply SQL](https://sqlmesh.readthedocs.io/en/stable/concepts/models/sql_models/#sql-based-definition) (no need for redundant and confusing `Jinja` + `YAML`) * See impact of changes before you run them in your warehouse with column-level lineage For more information, check out the [documentation](https://sqlmesh.readthedocs.io/en/stable/). ## Getting Started Install SQLMesh through [pypi](https://pypi.org/project/sqlmesh/) by running: ```bash mkdir sqlmesh-example cd sqlmesh-example python -m venv .venv source .venv/bin/activate pip install 'sqlmesh[lsp]' # install the sqlmesh package with extensions to work with VSCode source .venv/bin/activate # reactivate the venv to ensure you're using the right installation sqlmesh init # follow the prompts to get started (choose DuckDB) ``` > Note: You may need to run `python3` or `pip3` instead of `python` or `pip`, depending on your python installation.
Windows Installation ```bash mkdir sqlmesh-example cd sqlmesh-example python -m venv .venv .\.venv\Scripts\Activate.ps1 pip install 'sqlmesh[lsp]' # install the sqlmesh package with extensions to work with VSCode .\.venv\Scripts\Activate.ps1 # reactivate the venv to ensure you're using the right installation sqlmesh init # follow the prompts to get started (choose DuckDB) ```
Follow the [quickstart guide](https://sqlmesh.readthedocs.io/en/stable/quickstart/cli/) to learn how to use SQLMesh. You already have a head start! Follow the [crash course](https://sqlmesh.readthedocs.io/en/stable/examples/sqlmesh_cli_crash_course/) to learn the core movesets and use the easy to reference cheat sheet. Follow this [example](https://sqlmesh.readthedocs.io/en/stable/examples/incremental_time_full_walkthrough/) to learn how to use SQLMesh in a full walkthrough. ## Join Our Community Connect with us in the following ways: * Join the [Tobiko Slack Community](https://tobikodata.com/slack) to ask questions, or just to say hi! * File an issue on our [GitHub](https://github.com/SQLMesh/sqlmesh/issues/new) * Send us an email at [hello@tobikodata.com](mailto:hello@tobikodata.com) with your questions or feedback * Read our [blog](https://tobikodata.com/blog) ## Contributing We welcome contributions! Please see [CONTRIBUTING.md](https://github.com/SQLMesh/sqlmesh/blob/HEAD/CONTRIBUTING.md) for guidelines on how to contribute, including our DCO sign-off requirement. Please review our [Code of Conduct](https://github.com/SQLMesh/sqlmesh/blob/HEAD/CODE_OF_CONDUCT.md) and [Governance](https://github.com/SQLMesh/sqlmesh/blob/HEAD/GOVERNANCE.md) documents. [Read more](https://sqlmesh.readthedocs.io/en/stable/development/) on how to set up your development environment. ## License This project is licensed under the [Apache License 2.0](https://github.com/SQLMesh/sqlmesh/blob/HEAD/LICENSE). Documentation is licensed under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).