Data Lake Architecture AWS S3 + Glue + Athena

The Problem This Blueprint Solves

Your organization has data scattered across 15 operational databases, three SaaS platforms, and a legacy data warehouse that takes 6 hours to refresh. Business analysts wait days for reports. Data scientists cannot access raw data without filing tickets. Your "data lake" is actually a data swamp — terabytes of unstructured Parquet files in S3 with no catalog, no governance, and no way to know if the data is fresh or stale.

This blueprint is the data lake architecture I built for a logistics company ingesting 2.8TB daily from 23 source systems, supporting 140 analysts and data scientists with query response times under 12 seconds on datasets exceeding 500 billion rows.

What You Get

• Architecture diagrams — Medallion architecture (bronze/silver/gold layers), ingestion pipelines, catalog structure, access control model, and query engine topology (Draw.io)
• Terraform modules — S3 bucket hierarchy with lifecycle policies, AWS Glue crawlers and ETL jobs, Lake Formation permissions, Athena workgroups, and Redshift Spectrum external schema configuration
• Data governance framework — Classification taxonomy, PII detection patterns, retention policy templates, and Lake Formation tag-based access control setup
• Cost model — Storage tiering strategy (S3 Standard → IA → Glacier), query cost projections by workgroup, and Glue DPU optimization guidelines

Key Architecture Decisions

• Medallion architecture over flat landing zones — Bronze holds raw data as-ingested. Silver holds cleaned, deduplicated, schema-enforced data. Gold holds business-aggregated datasets. This layered approach means you can always reprocess from bronze if transformation logic changes, without re-ingesting from source systems.
• Lake Formation over IAM policies for data access — IAM policies for data lake access become unmanageable at 20+ tables and 50+ users. Lake Formation provides column-level and row-level access control with a centralized permission model that compliance teams can actually audit.
• Apache Iceberg table format over raw Parquet — Iceberg gives you ACID transactions, time travel queries, schema evolution, and partition evolution without rewriting data. Raw Parquet requires full partition rewrites for any schema change and provides no transaction guarantees for concurrent writers.
• Glue ETL over custom Spark clusters — Unless you need Spark tuning beyond what Glue provides, self-managed EMR clusters add operational burden without proportional benefit. Glue auto-scales, requires no cluster management, and costs are per-DPU-hour with no idle charges.

Who This Blueprint Is For

• Data Engineers building a centralized data platform for the first time
• Analytics Engineering Managers replacing a legacy data warehouse
• Data Platform Architects designing for 50+ concurrent analyst users
• Compliance Officers who need auditable data access controls for SOC 2 or HIPAA

Your First 48 Hours

Deploy the S3 bucket hierarchy and Glue catalog Terraform modules into a sandbox account. Upload the included sample dataset (synthetic e-commerce transactions) to the bronze layer. Run the Glue crawler to auto-detect schema, then execute the provided silver-layer ETL job to clean and partition the data. On day two, configure an Athena workgroup and run the sample queries against both bronze and silver layers. Compare query performance and cost — this demonstrates the value of the medallion architecture with real numbers.

Limitations and Trade-offs

Apache Iceberg table maintenance (compaction, snapshot expiration) must be scheduled — the blueprint includes Glue jobs for this, but tuning compaction frequency depends on your write volume. Athena query costs scale with data scanned; without partition pruning, a full-table scan on a 10TB dataset costs $50 per query. The included partition strategy assumes time-series data — if your primary access pattern is not time-based, you will need to redesign the partition scheme. Lake Formation does not yet support cross-account access with row-level filtering for all query engines.