integration-patterns-skill

More from dtsong/data-engineering-skills

• data-observability

  Use this skill when implementing monitoring, alerting, and incident response for data pipelines. Covers freshness monitoring, volume anomaly detection, schema change detection, alerting patterns, and incident response workflows. Common phrases: \"data freshness\", \"pipeline monitoring\", \"data anomaly\", \"schema drift\", \"data alerting\", \"incident response\", \"data observability\", \"stale data\". Do NOT use for writing dbt models (use dbt-transforms), pipeline scheduling (use data-pipelines), or data quality testing as deliverables (use data-testing).

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• streaming-data-skill

  Use this skill when building real-time or near-real-time data pipelines. Covers Kafka, Flink, Spark Streaming, Snowpipe, BigQuery streaming, materialized views, and batch-vs-streaming decisions. Common phrases: \"real-time pipeline\", \"Kafka consumer\", \"streaming vs batch\", \"low latency ingestion\". Do NOT use for batch integration patterns (use integration-patterns-skill) or pipeline orchestration (use data-orchestration-skill).

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