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SQLMesh

CLI

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.

SQLMesh/sqlmesh3.3k
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README

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.

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

Virtual Data Environments
  • 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 / 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

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
  • 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. Transpile Example
  • 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 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 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 
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

Connect with us in the following ways:

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines on how to contribute, including our DCO sign-off requirement.

Please review our Code of Conduct and Governance documents.

Read more on how to set up your development environment.

License

This project is licensed under the Apache License 2.0. Documentation is licensed under CC-BY-4.0.

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