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sqlmesh

Plan and deploy SQLMesh models across raw_zone, trusted_zone, and refined_zone.

36|12|Updated Nov 7, 2018
Search Tags:#debugging#data-modeling#cron#backfill#sqlmesh#data-ops#incremental-by-time-range
One-click install
npx skills add https://github.com/covoiturage-gouv-fr/mono --skill sqlmesh-covoiturage-gouv-fr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sqlmesh
Source: https://github.com/covoiturage-gouv-fr/mono/tree/main/.claude/skills/sqlmesh
Command: npx skills add https://github.com/covoiturage-gouv-fr/mono --skill sqlmesh-covoiturage-gouv-fr

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

SQLMesh enables data teams to manage complex SQL-based data models with reproducible production workflows, ensuring consistent plan/run cycles across raw_zone, trusted_zone, and refined_zone.

Core Features & Use Cases

  • Model patterns and zone architecture across raw_zone, trusted_zone, and refined_zone to simplify governance and testing.
  • Safe indexing and performance guarantees via the @create_indexes() macro, avoiding common deadlocks.
  • Cron-driven execution and backfill control for incremental models, including lookback handling.
  • Debugging and observability conventions for diagnosing failed plans, time-bound processing, and timezone handling.

Quick Start

Plan and deploy the first SQLMesh model in a development environment to validate the plan, dependencies, and backfill behavior.

Frequently Asked Questions about sqlmesh

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate SQL data models for reproducible production pipelines?▼

SQLMesh orchestrates SQL data models across raw_zone, trusted_zone, and refined_zone to ensure reproducible, time-bounded production pipelines with consistent plan and run cycles.

How do I configure incremental by time range backfills and cron schedules?▼

Configure cron-driven execution for incremental models and use exact time boundaries with @start_ts and @end_ts to control backfills, including lookback handling and safe timezone operations.

What is the best way to manage indexes and avoid deadlocks during SQL pipeline runs?▼

Manage indexes safely using the @create_indexes() macro, which provides performance guarantees while avoiding common deadlocks encountered during complex SQLMesh model plan and run cycles.

How do I debug failed plans and time-bound processing in SQL data pipelines?▼

Debug failed plans using established observability conventions to diagnose time-bound processing, enforce non-interval time filtering on start_datetime, and validate exact time boundaries.

Why does my time-bounded pipeline fail when using interval time filtering?▼

Pipelines fail because the system requires non-interval time filtering on start_datetime and enforces exact time boundaries using @start_ts and @end_ts to maintain reproducible backfill operations.

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