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.

Core Features

Get instant SQL impact and context of your changes, both in the CLI and in the SQLMesh VSCode Extension

Virtual Data Environments

• See a full diagram of how Virtual Data Environments work
• Watch this video to learn more

• Create isolated development environments without data warehouse costs
• Plan / Apply workflow like Terraform to understand potential impact of changes
• Easy to use CI/CD bot 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

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

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 /100AS amount, /* `amount` is currently stored in cents, so we convert it to dollars */'new_column'AS new_column, /* non-breaking change example */FROMtcloud_demo.seed_raw_payments

test_stg_payments:
model: tcloud_demo.stg_paymentsinputs:
tcloud_demo.seed_raw_payments:
- id: 66order_id: 58payment_method: couponamount: 1800
- id: 27order_id: 24payment_method: couponamount: 2600
- id: 30order_id: 25payment_method: couponamount: 1600
- id: 109order_id: 95payment_method: couponamount: 2400
- id: 3order_id: 3payment_method: couponamount: 100outputs:
query:
- payment_id: 66order_id: 58payment_method: couponamount: 18.0new_column: new_column
- payment_id: 27order_id: 24payment_method: couponamount: 26.0new_column: new_column
- payment_id: 30order_id: 25payment_method: couponamount: 16.0new_column: new_column
- payment_id: 109order_id: 95payment_method: couponamount: 24.0new_column: new_column
- payment_id: 3order_id: 3payment_method: couponamount: 1.0new_column: new_column

• Never build a table more than once
• Track what data’s been modified and run only the necessary transformations for incremental models
• Run unit tests for free and configure automated audits
• Run table diffs 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.

• Debug transformation errors before you run them in your warehouse in 10+ different SQL dialects
• Definitions using simply SQL (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 website and documentation.

Getting Started

Install SQLMesh through pypi by running:

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 VSCodesource .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

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 to learn how to use SQLMesh. You already have a head start!

Follow the crash course to learn the core movesets and use the easy to reference cheat sheet.

Follow this example to learn how to use SQLMesh in a full walkthrough.

Join Our Community

Together, we want to build data transformation without the waste. Connect with us in the following ways:

• Join the Tobiko Slack Community to ask questions, or just to say hi!
• File an issue on our GitHub
• Send us an email at hello@tobikodata.com with your questions or feedback
• Read our blog

Contribution

Contributions in the form of issues or pull requests (from fork) are greatly appreciated.

Read more on how to contribute to SQLMesh open source.

Watch this video walkthrough to see how our team contributes a feature to SQLMesh.