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AlexanderStephenThompson
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data-aws

Standardize AWS data infrastructure patterns for S3, Glue, Lambda, Athena, Redshift, and Step Functions.

Updated Aug 27, 2026
Search Tags:#ETL#AWS#architecture-patterns#cost-optimization#analyze#data-lake
One-click install
npx skills add https://github.com/AlexanderStephenThompson/claude-hub --skill data-aws
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-aws
Source: https://github.com/AlexanderStephenThompson/claude-hub/tree/main/data/skills/data-aws
Command: npx skills add https://github.com/AlexanderStephenThompson/claude-hub --skill data-aws

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill standardizes AWS data infrastructure patterns to help teams design efficient data pipelines and analytics while controlling costs.

Core Features & Use Cases

  • S3 Data Lake Patterns: Guidelines for raw/staged/curated data with partitioned storage and Parquet formats.
  • ETL & Orchestration: Glue, Lambda, and Step Functions patterns for reliable, incremental data processing.
  • Analytics & Storage: Athena for ad-hoc queries and Redshift for BI workloads, with cost-conscious strategies.
  • Use Case: Model a typical data workflow from raw ingest to curated analytics, with explicit schemas and data governance.

Quick Start

Analyze your current AWS data stack and generate a recommended data-lake, ETL, and analytics pattern.

Frequently Asked Questions about data-aws

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the best way to organize an S3 data lake to minimize AWS pipeline costs?▼

The best way to minimize S3 data lake costs is to standardize raw, staged, and curated data layers using partitioned storage and Parquet formats. This approach reduces scan times and optimizes downstream Athena queries.

How do I orchestrate Glue ETL and Lambda processing for incremental data workloads?▼

You can orchestrate incremental data processing by applying Step Functions patterns to coordinate Glue ETL and Lambda processing. This standardizes reliable, incremental workflows across common analytics pipelines.

When should I use Athena versus Redshift for querying AWS analytics workloads?▼

Use Athena for ad-hoc queries on S3 data lakes, and use Redshift for heavier BI workloads. Applying cost-conscious strategies and explicit schemas to both ensures optimal performance for your analytics tasks.

Does this approach require explicit schemas and partitioned data for data governance?▼

Yes, standardizing AWS data infrastructure requires explicit schemas and partitioned data. Enforcing these best practices informs architecture decisions and ensures proper data governance across your analytics workflows.

Can I model a typical data workflow from raw ingest to curated analytics using AWS services?▼

Yes, you can model a complete data workflow from raw ingest to curated analytics. This involves structuring S3 data lakes, orchestrating Glue and Lambda, and querying via Athena or Redshift.

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