How does the pipeline orchestration work?

It uses a topological sort algorithm to ensure that pipeline steps are executed in the correct order based on defined dependencies, skipping steps if their prerequisites fail.

Does this skill support parallel data processing?

Yes, it includes patterns for parallel backfill operations using Python's ThreadPoolExecutor to handle date-range migrations with controlled concurrency.

Can I implement real-time synchronization with this skill?

The skill provides Change Data Capture (CDC) patterns that handle INSERT, UPDATE, and DELETE operations, making it suitable for near real-time data syncs.

What is the ETL Incremental Patterns skill?

It is a specialized capability for Claude Code that provides standardized templates and logic for efficient data loading, backfilling, and pipeline management.

ETL Incremental Patterns

Name: ETL Incremental Patterns
Author: majesticlabs-dev

bymajesticlabs-dev

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Database Management

Implements efficient data loading strategies including incremental syncs, CDC, and parallel backfill operations.

About

The ETL Incremental Patterns skill provides a comprehensive library of data engineering patterns designed to optimize data movement and pipeline reliability. It enables Claude to implement various synchronization strategies, from simple timestamp-based increments to complex Change Data Capture (CDC) and zero-downtime table swaps. Additionally, it features a lightweight orchestration framework for managing task dependencies and a parallelized backfill engine, making it an essential tool for data engineers building scalable, production-grade data pipelines and migrations.

Key Features

Change Data Capture (CDC) patterns for real-time data synchronization
Zero-downtime 'Full Refresh with Swap' table management patterns
Topological sort-based pipeline orchestration for complex task dependencies
Parallel backfill strategies for high-performance historical data migration
Timestamp-based incremental loading logic for efficient batch processing
19 GitHub stars

Use Cases

Migrating large-scale historical datasets through controlled, multi-threaded backfills
Building automated data pipelines with robust error handling and dependency management
Synchronizing transactional databases with data warehouses using incremental updates

About

Key Features

Change Data Capture (CDC) patterns for real-time data synchronization
Zero-downtime 'Full Refresh with Swap' table management patterns
Topological sort-based pipeline orchestration for complex task dependencies
Parallel backfill strategies for high-performance historical data migration
Timestamp-based incremental loading logic for efficient batch processing
19 GitHub stars

Use Cases

Migrating large-scale historical datasets through controlled, multi-threaded backfills
Building automated data pipelines with robust error handling and dependency management
Synchronizing transactional databases with data warehouses using incremental updates