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Overview

Realistic examples are a fantastic way to understand SQLMesh better.

They allow you to tinker with a project's code and data, issuing different SQLMesh commands to see what happens.

You can reset the examples at any time, so if things get turned around you can just start over!

This page links to a few different types of examples:

  • Walkthroughs pose a specific story or task, and you follow along as we work through the story
    • Walkthroughs do not require running code, although the code is available if you would like to
    • Different walkthroughs use different SQL engines, so if you want to run the code you might need to update it for your SQL engine
  • Projects are self-contained SQLMesh projects and datasets
    • Projects generally use DuckDB so you can run them locally without installing or accessing a separate SQL engine

!!! tip

If you haven't tried out SQLMesh before, we recommending working through the [SQLMesh Quickstart](../quick_start.md) before trying these examples!

Walkthroughs

Walkthroughs are easy to follow and provide lots of information in a self-contained format.

Projects

SQLMesh example projects are stored in the sqlmesh-examples Github repository. The repository's front page includes additional information about how to download the files and set up the projects.

The two most comprehensive example projects use the SQLMesh sushi data, based on a fictional sushi restaurant. ("Tobiko" is the Japanese word for flying fish roe, commonly used in sushi.)

The sushi data is described in an overview notebook in the repository.

The example repository include two versions of the sushi project, at different levels of complexity:

  • The simple project contains four VIEW and one SEED model
    • The VIEW model kind refreshes every run, making it easy to reason about SQLMesh's behavior
  • The moderate project contains five INCREMENTAL_BY_TIME_RANGE, one FULL, one VIEW, and one SEED model
    • The incremental models allow you to observe how and when new data is transformed by SQLMesh
    • Some models, like customer_revenue_lifetime, demonstrate more advanced incremental queries like customer lifetime value calculation