sqlmesh

What problem does it solve?

Building and managing complex data transformation pipelines often lacks features like automatic DAG inference, efficient incremental processing, and isolated testing environments, leading to slower development and higher costs. This Skill provides SQLMesh patterns to address these challenges.

Core Features & Use Cases

• Project Structure: Guidelines for organizing SQLMesh projects, including models, macros, seeds, audits, and tests.
• Model Definition: Examples for defining various model kinds, such as FULL, INCREMENTAL_BY_TIME_RANGE, and VIEW models.
• Virtual Environments: How to leverage virtual environments for isolated testing and development of data transformations.
• Audits: Patterns for defining data quality audits to ensure the integrity of your data models.
• Best Practices: Tips for using column-level lineage for impact analysis and optimizing with incremental models.
• Use Case: Designing a new data transformation project, implementing an incremental data model for a large dataset, setting up virtual environments for data testing, or defining data quality audits for critical tables.

Quick Start

Use the sqlmesh skill to generate a SQLMesh model definition for an incremental table based on a created_at timestamp.