What is idempotency in an ETL context?

Idempotency ensures that running the same ETL process multiple times with the same input yields the same result without duplicating data or causing database errors.

How does this skill handle memory issues with large datasets?

It provides patterns for chunked processing and memory-efficient file reading to handle datasets that are too large to fit into system memory at once.

What is a Dead Letter Queue (DLQ) pattern?

A DLQ is a pattern where records that fail processing are captured and stored separately with error metadata, allowing the main pipeline to continue while providing a path for debugging and re-processing.

Does this skill help with API failures?

Yes, it includes standardized retry logic patterns with exponential backoff to handle transient network issues or API rate limits gracefully.

ETL Core Reliability Patterns

Name: ETL Core Reliability Patterns
Author: majesticlabs-dev

bymajesticlabs-dev

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Data Science & ML

Implements production-grade ETL patterns including idempotency, checkpointing, and robust error handling for data pipelines.

About

The ETL Core Patterns skill equips Claude with specialized knowledge for building resilient data engineering pipelines. It provides implementation patterns for critical reliability features such as idempotent data loading (UPSERT, source hashing), checkpointing for stateful processing, memory-efficient chunking, and advanced retry logic with exponential backoff. This skill is essential for developers building production-level data integration workflows that require high durability, fault tolerance, and clear observability through structured logging.

Key Features

Advanced retry logic with configurable backoff and structured logging
Stateful checkpointing for resuming interrupted data syncs
Memory-efficient batch and chunked processing for large datasets
Idempotent loading strategies including UPSERT and Delete-then-Insert
Robust error handling with Dead Letter Queue (DLQ) implementation patterns
19 GitHub stars

Use Cases

Processing massive flat files without memory overflow using chunking
Building resilient data pipelines from external APIs to SQL databases
Synchronizing records between disparate systems while maintaining processing state

About

Key Features

Advanced retry logic with configurable backoff and structured logging
Stateful checkpointing for resuming interrupted data syncs
Memory-efficient batch and chunked processing for large datasets
Idempotent loading strategies including UPSERT and Delete-then-Insert
Robust error handling with Dead Letter Queue (DLQ) implementation patterns
19 GitHub stars

Use Cases

Processing massive flat files without memory overflow using chunking
Building resilient data pipelines from external APIs to SQL databases
Synchronizing records between disparate systems while maintaining processing state