Does it support incremental loading?

Yes, it provides strategies for timestamp-based increments, Change Data Capture (CDC) event processing, and full refreshes based on table size and business needs.

What reliability patterns are supported?

The skill includes patterns for idempotency, checkpointing, structured logging, error handling with DLQs, chunking for memory management, and retry logic with backoff.

Is this specific to a particular programming language?

While it provides Python-centric examples, the logic and patterns are applicable to any language or framework used for data engineering and ETL development.

When should I use the ETL Patterns skill?

Use it when designing, building, or debugging data pipelines to ensure they are robust, scalable, and capable of handling failures gracefully without data loss.

Can it help with large data migrations?

Absolutely, it includes specific patterns for parallel backfills using date ranges and zero-downtime swap patterns for high-volume migrations.

Production ETL Patterns

Name: Production ETL Patterns
Author: majesticlabs-dev

bymajesticlabs-dev

•

Database Management

Orchestrates production-grade Extract-Transform-Load (ETL) pipelines using reliability patterns and incremental load strategies.

This skill serves as a central orchestrator for building robust, production-ready data pipelines. It provides structured guidance on implementing essential reliability features like idempotency, checkpointing, and error handling with Dead Letter Queues (DLQ). Additionally, it helps developers select and implement the most efficient loading strategies based on data volume and update frequency, ranging from simple full refreshes to advanced Change Data Capture (CDC) and parallel backfills, ensuring data integrity and pipeline resilience in professional data engineering environments.

Key Features

01Optimizes memory management through chunked processing

02Implements reliability patterns like idempotency and checkpointing

03Automates pipeline orchestration and zero-downtime refreshes

04Supports diverse load strategies including CDC and timestamp-based increments

0518 GitHub stars

06Provides error handling and Dead Letter Queue (DLQ) patterns

Use Cases

01Building resilient data pipelines that can resume automatically after a failure

02Syncing large databases efficiently using incremental or CDC strategies

03Migrating historical data securely with parallel backfill patterns

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add majesticlabs-dev/majestic-marketplace etl-patterns

For use in Claude.ai and ChatGPT

Download Skill