data-engineering
De la autor
ETL/ELT pipelines, data warehousing (BigQuery, Snowflake, Redshift), stream processing (Kafka, Spark Streaming), orchestration (Airflow, Dagster, Prefect), dbt transformations, and data lake architecture. Use when building data pipelines, designing warehouse schemas, or implementing real-time data processing.
Instalare
npx skills add travisjneuman/.claude --skill data-engineering -a claude-code
Se instalează în .claude/skills/data-engineering/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
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aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- tool.data_engineering.134100a5a52f2e93AI45Lab/OpenARTData 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).Date★ 253
- tool.data_engineering.55c8f35d7b3cac72AI45Lab/OpenARTData 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).Date★ 253
- senior-data-engineerborghei/Claude-SkillsData engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use when designing data architectures, building pipelines, adding data-quality checks, optimizing ETL/ELT, or troubleshooting pipeline failures.Date★ 971
- engineering-data-pipelinestelagod/code-abyssData 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.Date★ 242
- agent-data-engineertravisjneuman/.claudeSpecialist subagent: ETL pipelines, data warehousing, stream processing, and data infrastructure specialist. Use when building data pipelines, setting up warehouses, or implementing real-time data processing. Trigger phrases: ETL, pipeline, data warehouse, BigQuery, Snowflake, Redshift, Kafka, Airflow, dbt, streaming, data lake, data model, star schema.DevOps★ 106
- nw-data-architecture-patternsnWave-ai/nWaveData architecture patterns (warehouse, lake, lakehouse, mesh), ETL/ELT pipelines, streaming architectures, scaling strategies, and schema design patternsPlanificare★ 622
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