What does this skill do?

The Data Engineering skill provides an agent with practical patterns for building robust data pipelines, data-warehouse architecture, and data quality in production.

It covers a class-based ETL pipeline pattern, Apache Spark processing with adaptive query execution and partitioning, data-quality checks with thresholds and severity, and star-schema modeling with slowly changing dimensions.

It emphasizes batch over row-by-row processing, incremental loads with watermarks, idempotent operations, and data lineage.

  • Structures ETL with clear extract, transform, and load stages

  • Processes large data with partitioned Spark jobs

  • Validates data quality between pipeline stages

  • Models star schemas with slowly changing dimensions

How it works

1

Structure the ETL

The agent separates extraction, transformation, and loading, tracking records processed, errors, and duration.

2

Process at scale

It uses Apache Spark with adaptive query execution, appropriate partitioning, and partitioned writes to cloud storage.

3

Validate quality

It applies data-quality checks with configurable thresholds and severity — nulls, negatives, duplicates, and freshness — between stages.

4

Model the warehouse

It implements star schemas with dimension and fact tables and slowly changing dimensions, using incremental loads.

Best use cases for this skill

  • ETL pipelines: Build or maintain ETL/ELT pipelines with clear stages.

  • Spark processing: Process large datasets with partitioned Spark jobs.

  • Data quality: Add validation with thresholds between pipeline stages.

  • Warehousing: Design star schemas with slowly changing dimensions.

Frequently asked questions

What does the Data Engineering skill do?

It provides data-engineering patterns for ETL pipelines, data warehousing, Apache Spark processing, and data-quality validation in production environments.

When should an agent use it?

Use it when building or maintaining ETL/ELT pipelines, designing warehouse schemas, processing large datasets with Spark, or implementing data-quality frameworks.

What anti-patterns does it avoid?

Row-by-row processing, missing partitioning, skipping quality checks between stages, loading raw data into warehouses, full table scans, and absent lineage.

Does it support incremental loads?

Yes. It uses incremental processing with watermarks and idempotent load operations, with automated freshness monitoring.

Use from source

Ask your Buda agent to install this community skill.

Copy this prompt into your own Buda agent. It includes the source repository and install command so the agent can complete the setup in natural language.

Prompt to send to your Buda agent
Please install the "Data Engineering Skill" skill into this Buda agent.

Source repository: https://github.com/rohitg00/awesome-claude-code-toolkit
Install command: npx skills add rohitg00/awesome-claude-code-toolkit --skill data-engineering -y -a universal

Please run the install command, verify the skill is available, and tell me any setup notes or follow-up steps from the source repository.
Explore marketplace

Install workflow

  1. 1Copy the prompt
  2. 2Send it to your Buda agent
  3. 3Done