Which data warehouses are supported by this skill?

The skill includes best practices and syntax optimization for popular platforms including Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse.

How does this skill improve data quality?

It integrates best practices for data validation, metadata documentation, and pipeline monitoring to ensure your data infrastructure remains reliable and accurate.

Can this skill help with real-time data streaming?

Yes, it offers implementation patterns and configuration guidance for streaming technologies like Apache Kafka, AWS Kinesis, and Apache Pulsar.

Does it include guidance for data transformation tools?

Absolutely, it covers modern transformation frameworks like dbt (data build tool) and data processing engines such as Apache Spark, Pandas, and Polars.

What does the Data Engineering skill do in Claude Code?

It provides domain-specific knowledge and implementation patterns for building data pipelines, managing warehouses, and optimizing SQL queries within the Claude CLI environment.

Data Engineering & Analytics

Name: Data Engineering & Analytics
Author: pluginagentmarketplace

bypluginagentmarketplace

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Gestión de Bases de Datos

Architects robust data pipelines, optimizes SQL queries, and manages large-scale ETL/ELT workflows across modern data warehouses.

Acerca de

The Data Engineering & Analytics skill provides Claude with deep expertise in designing and maintaining scalable data infrastructure. It offers specialized guidance for building efficient ETL/ELT pipelines, optimizing complex SQL queries for platforms like Snowflake and BigQuery, and implementing best practices for data quality and governance. Whether you are working with streaming data via Kafka or orchestrating transformations with dbt and Airflow, this skill ensures your data systems are performant, secure, and reliable.

Características Principales

Expert guidance for ETL and ELT pipeline architecture
Advanced SQL optimization for major data warehouses
Integration patterns for dbt, Airflow, Spark, and Kafka
1 GitHub stars
Best practices for data governance, security, and quality
Scalable data modeling and warehousing strategies

Casos de Uso

Refactoring inefficient SQL queries to reduce latency and cloud compute costs
Developing automated pipelines to ingest and transform raw data for business intelligence
Implementing real-time data streaming architectures for live analytics

Acerca de

Características Principales

Expert guidance for ETL and ELT pipeline architecture
Advanced SQL optimization for major data warehouses
Integration patterns for dbt, Airflow, Spark, and Kafka
1 GitHub stars
Best practices for data governance, security, and quality
Scalable data modeling and warehousing strategies

Casos de Uso

Refactoring inefficient SQL queries to reduce latency and cloud compute costs
Developing automated pipelines to ingest and transform raw data for business intelligence
Implementing real-time data streaming architectures for live analytics