AWS provides the definitive data lake platform, with S3 as the infinitely scalable storage layer, Lake Formation for governance, Glue for ETL, Athena for serverless SQL queries, and EMR for distributed processing. S3's durability (99.999999999%), tiered storage classes, and...
ZTABS builds data lake architecture with AWS — delivering production-grade solutions backed by 500+ projects and 10+ years of experience. AWS provides the definitive data lake platform, with S3 as the infinitely scalable storage layer, Lake Formation for governance, Glue for ETL, Athena for serverless SQL queries, and EMR for distributed processing. S3's durability (99.999999999%), tiered storage classes, and pay-per-GB pricing make it the ideal foundation for storing petabytes of structured, semi-structured, and unstructured data. Get a free consultation →
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AWS is a proven choice for data lake architecture. Our team has delivered hundreds of data lake architecture projects with AWS, and the results speak for themselves.
AWS provides the definitive data lake platform, with S3 as the infinitely scalable storage layer, Lake Formation for governance, Glue for ETL, Athena for serverless SQL queries, and EMR for distributed processing. S3's durability (99.999999999%), tiered storage classes, and pay-per-GB pricing make it the ideal foundation for storing petabytes of structured, semi-structured, and unstructured data. The lakehouse pattern on AWS combines the cost efficiency of S3 storage with the query performance of columnar formats like Parquet and Iceberg, giving organizations a unified analytics platform.
S3 stores any volume of data—from gigabytes to exabytes—without capacity planning. Storage classes (Standard, Intelligent-Tiering, Glacier) automatically optimize cost based on access patterns, with lifecycle policies moving aged data to cheaper tiers.
Athena runs SQL queries directly on S3 data without provisioning clusters. You pay $5 per TB scanned, and queries on Parquet-formatted data with partitioning scan 90%+ less data, making ad-hoc analytics extremely cost-effective.
Lake Formation centralizes access control, auditing, and data cataloging. Fine-grained permissions control access at the column and row level, and every data access is logged for compliance auditing.
Apache Iceberg on S3 provides ACID transactions, schema evolution, and time travel on data lake tables. This brings data warehouse reliability to the data lake without the cost of a proprietary warehouse.
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Schedule a CallAdopt Apache Iceberg as your table format from day one. Iceberg provides schema evolution, partition evolution, and time travel that plain Parquet files lack. Migrating from raw Parquet to Iceberg later requires rewriting every table, so starting with Iceberg saves significant future effort.
AWS has become the go-to choice for data lake architecture because it balances developer productivity with production performance. The ecosystem maturity means fewer custom solutions and faster time-to-market.
| Layer | Tool |
|---|---|
| Storage | Amazon S3 |
| ETL | AWS Glue / dbt |
| Catalog | Glue Data Catalog |
| Query | Amazon Athena |
| Processing | EMR (Spark) / Glue Spark |
| Governance | Lake Formation |
An AWS data lake architecture uses S3 as the central storage layer organized into landing, raw, curated, and consumption zones. Data arrives in the landing zone from diverse sources—Kinesis Data Firehose streams real-time events, AWS Database Migration Service replicates database changes, and S3 Transfer handles batch file uploads. Glue crawlers discover schema in the landing zone and register tables in the Glue Data Catalog, making data queryable by Athena immediately.
Glue ETL jobs or EMR Spark jobs transform landing data into Parquet format with partitioning by date and key dimensions, writing to the raw zone. A second transformation layer applies business logic, joins reference data, and deduplicates records into the curated zone using dbt models running on Athena or Spark. Apache Iceberg manages curated tables with ACID transactions, enabling reliable upserts and providing time travel for regulatory queries that need point-in-time data snapshots.
Lake Formation governs access with column-level security—analysts see only the columns their role permits, and PII columns are masked or excluded automatically. Athena serves as the primary query interface for analysts and BI tools like QuickSight, with queries on well-partitioned Iceberg tables scanning minimal data. The Data Catalog serves as the single source of truth for data lineage, quality metrics, and business glossary terms across the entire lake.
Our senior AWS engineers have delivered 500+ projects. Get a free consultation with a technical architect.