Data Lake Storage

Architecture

Build a modern data lake on S3:

• S3 for raw, processed, and curated data layers
• AWS Glue for data cataloging and ETL
• Athena for serverless SQL queries
• Lake Formation for governance and security

When to Use

This pattern is ideal when you need:

• Centralized data repository for analytics
• Support for structured and unstructured data
• Integration with big data processing tools
• Cost optimization through storage tiering
• Schema-on-read flexibility

Data Organization

data-lake/
  raw/           # Raw ingested data
  processed/     # Transformed data
  curated/       # Business-ready datasets
  temp/          # Temporary processing data

Common Integrations

• EMR for Spark and Hadoop processing
• Redshift Spectrum for data warehouse extension
• SageMaker for machine learning
• QuickSight for business intelligence

Considerations

• Use consistent partitioning strategies
• Implement lifecycle rules for cost optimization
• Enable versioning for data lineage
• Use Parquet or ORC for analytical workloads