This skill provides a comprehensive library of domain-specific patterns for ingesting data into systems from external sources like S3, GCS, REST APIs, and Kafka. It bridges the gap between raw data sources and structured databases by offering implementation guidance for Python, TypeScript, Rust, and Go, covering essential ETL/ELT strategies, batch processing, and Change Data Capture (CDC). Whether you are migrating a legacy database, processing high-performance Parquet files, or building a real-time streaming pipeline, this skill ensures best practices for idempotency, backpressure handling, and schema validation are applied correctly.
Key Features
01Multi-language support for Python, TypeScript, Go, and Rust implementation
02Standardized patterns for Cloud Storage (S3, GCS, and Azure Blob)
03API polling and webhook receiver implementation templates
04High-performance file processing using Polars and dlt (data load tool)
05158 GitHub stars
06Real-time streaming ingestion logic for Kafka and Kinesis
Use Cases
01Automating ETL pipelines to move CSV, JSON, and Parquet files into SQL/NoSQL databases
02Implementing Change Data Capture (CDC) for seamless database migrations and replication
03Building robust API consumers with built-in rate limiting, pagination, and backoff
