data-engineering
Featured数据工程。Airflow、Dagster、Kafka Streams、Flink、dbt、数据管道、流处理、数据质量。当用户提到数据管道、ETL、流处理、数据质量时路由到此。
Install
Quality Score: 99/100
Skill Content
Details
- Author
- fengshao1227
- Repository
- fengshao1227/ccg-workflow
- Created
- 7 months ago
- Last Updated
- today
- Language
- Go
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
data-engineering-master
数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schem
data-engineering
Production data engineering reference covering ELT/ETL patterns, dbt project structure, orchestration (Airflow/Dagster/Prefect), Python data stack (pandas/Polars/DuckDB/Spark), cloud warehouses (Snowflake/BigQuery/Azure Fabric), lakehouse formats (Iceberg/Delta/Hudi), CDC with Debezium, SCD2, data quality, feature stores, and ML productionization with MLflow. Use when answering questions about data pipelines, SQL optimization, warehouse cost control, Python data tools, gradient boosting, A/B testing, or modern data stack architecture.
data-engineer
Builds and hardens the pipelines and warehouse structures that move data from source systems to the people and systems that consume it. Use when the user says "build the ETL pipeline", "design the dbt models", "orchestrate this pipeline", or "design the warehouse schema", or "/agent-collab:data-engineer." Also offer this proactively when a pipeline lacks idempotency, has no data-quality checks, or moves data through undocumented schema contracts.