databricks-spark-structured-streaming
Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.
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data-pipeline
claude-office-skills/skills
Data pipeline and ETL automation - extract, transform, load workflows for data integration and analytics
ETL Pipeline
claude-office-skills/skills
Design and automate Extract, Transform, Load data pipelines for data integration and analytics
data-throughput-accelerator
affaan-m/ecc
Use when large data ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, or table synchronization needs to become much faster while preserving data correctness.
