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name: sqlmesh description: SQLMesh patterns for data transformation with column-level lineage and virtual environments. Use when building data pipelines that need advanced features like automatic DAG inference and efficient incremental processing.

SQLMesh Skill

This skill provides SQLMesh patterns for data transformation.

Project Structure

sqlmesh_project/
├── config.yaml
├── models/
│   ├── staging/
│   │   └── stg_customers.sql
│   └── marts/
│       └── dim_customers.sql
├── macros/
├── seeds/
├── audits/
└── tests/

Model Definition

-- models/staging/stg_customers.sql
MODEL (
    name staging.stg_customers,
    kind INCREMENTAL_BY_TIME_RANGE (
        time_column created_at
    ),
    cron '@daily'
);

SELECT
    id AS customer_id,
    LOWER(email) AS email,
    created_at
FROM raw.customers
WHERE created_at BETWEEN @start_ds AND @end_ds

Model Kinds

Kind	Use Case
FULL	Complete refresh each run
INCREMENTAL_BY_TIME_RANGE	Time-based incremental
INCREMENTAL_BY_UNIQUE_KEY	Key-based merge
VIEW	Virtual table
SEED	Static CSV data

Virtual Environments

# Create a virtual environment for testing
sqlmesh plan dev

# Apply to production
sqlmesh plan prod

Audits

-- audits/no_nulls.sql
AUDIT (
    name assert_no_null_customer_id,
    model staging.stg_customers
);

SELECT * FROM staging.stg_customers
WHERE customer_id IS NULL

Best Practices

• Use column-level lineage for impact analysis
• Leverage virtual environments for testing
• Define audits for data quality
• Use incremental models for efficiency