engineering-data-pipelines

name	engineering-data-pipelines
description	Data engineering knowledge reference covering Airflow, Dagster, Kafka Streams, Flink, dbt, and data quality patterns. Use when building data pipelines, ETL workflows, stream processing, or data quality checks.
user-invocable	false

数据工程域 · Data Engineering

编排：Airflow(调度) | Dagster(资产) | Prefect(现代流)
流处理：Kafka Streams(嵌入式) | Flink(集群) | Spark Streaming
质量：Great Expectations | dbt tests | Soda Core

编排检查项

幂等(UPSERT/分区覆盖) | 增量(WHERE updated_at > last_run) | 事件驱动触发 | 跨 DAG 依赖 | 数据血缘(ref()/Asset deps)

流处理检查项

时间语义选择 | Watermark 乱序容忍 | 状态 TTL 防膨胀 | Checkpoint 间隔 | 端到端 Exactly-Once | 背压监控

质量检查项

分层验证(源→转换→目标) | 完整性+准确性+一致性 | 及时性阈值 | 加权评分 | 告警(Slack/PagerDuty)

工具对比、API 用法、质量维度详见 references/details.md