tool.data_engineering.55c8f35d7b3cac72
From the author
Data engineering practice patterns for data pipelines, ETL/ELT, orchestration, data quality, and infrastructure. Covers Airflow, Dagster, Prefect, Spark, dbt, data lakes, lakehouses, Iceberg, Delta Lake, warehouses (Snowflake, BigQuery, Databricks), streaming (Kafka, Kinesis, Flink), CDC, data contracts, data governance, data mesh, and batch processing. Use when reviewing or building data pipelines, ingestion systems, stream processing, data infrastructure, or data platform architecture. Do not use for SQL modeling or dbt projects (use analytics-engineering), business dashboards (use analytics), or ML model training (use data-science).
Install
npx skills add AI45Lab/OpenART --skill tool.data_engineering.55c8f35d7b3cac72 -a claude-code
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