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

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Gestión de Bases de Datos

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

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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.

Características Principales

Optimizes memory management through chunked processing
Implements reliability patterns like idempotency and checkpointing
Automates pipeline orchestration and zero-downtime refreshes
Supports diverse load strategies including CDC and timestamp-based increments
18 GitHub stars
Provides error handling and Dead Letter Queue (DLQ) patterns

Casos de Uso

Building resilient data pipelines that can resume automatically after a failure
Syncing large databases efficiently using incremental or CDC strategies
Migrating historical data securely with parallel backfill patterns

Acerca de

Características Principales

Optimizes memory management through chunked processing
Implements reliability patterns like idempotency and checkpointing
Automates pipeline orchestration and zero-downtime refreshes
Supports diverse load strategies including CDC and timestamp-based increments
18 GitHub stars
Provides error handling and Dead Letter Queue (DLQ) patterns

Casos de Uso

Building resilient data pipelines that can resume automatically after a failure
Syncing large databases efficiently using incremental or CDC strategies
Migrating historical data securely with parallel backfill patterns