Accelerating Data Lakehouse Transformation with Amazon S3 Tables, Apache Iceberg, and Lake Formation Governance

Managing large-scale data lakes often means struggling with fragmented pipelines, inconsistent access controls, and time-consuming operations. For organizations looking to modernize their architecture, the challenge is finding a solution that balances performance and governance.

As an AWS Premium Tier Partner, zeb helps businesses overcome these hurdles by simplifying their lakehouse transformation journey. By harnessing the power of Amazon S3 Tables with native Apache Iceberg support, combined with Lake Formation’s fine-grained governance and seamless analytics through Athena, we enable enterprises to modernize faster, turning complex architectures into efficient and insight-driven platforms.

Building a robust lakehouse architecture

Data zoning with S3 Table buckets

A multi-zone architecture on S3 enables clear separation of data processing stages:

• Raw Zone: Stores immutable, source-aligned data for traceability and audits.
• Stage Zone: Houses cleansed, normalized data processed through AWS Glue and orchestrated using Step Functions.
• Analytics Zone: Hosts aggregated, modeled datasets in star or snowflake schemas ready for BI tools and machine learning pipelines

This zoning strategy ensures structured data processing while supporting lineage tracking and rollback capabilities.

Table management with Iceberg and Athena

Using Amazon Athena, organizations can define, query, and evolve tables directly through SQL DDL. Apache Iceberg’s open table format enables:

• Schema evolution without disrupting downstream systems.
• ACID transactions for reliable data operations.
• Time travel capabilities to query historical snapshots.

This eliminates much of the complexity traditionally involved in managing large-scale, changing datasets.

Automating data pipelines with Glue and Step Functions

Data cleansing, transformation, and movement across zones are automated through AWS Glue jobs, coordinated by AWS Step Functions. This orchestration ensures consistency and timeliness of updates across datasets, crucial for accurate analytics and AI model training.

End-to-end governance framework

By integrating with AWS Lake Formation, Amazon S3 Tables bring granular governance to every part of the lakehouse:

• Apply access controls at the table, column, or row level.
• Secure roles and identities (including those used by Glue jobs).
• Centralize permissions and metadata for auditability and compliance.

This lets organizations maintain tight control even as data volume and usage scale dramatically.

From raw data to actional insights

With Athena’s seamless connection to Amazon QuickSight, business users can interact with governed datasets through intuitive, AI-powered dashboards. This extends the lakehouse to end users, empowering teams to explore data without needing engineering support.

What it means for your business

1. Scalable Performance for Growing Workloads

• Faster Queries: S3 Tables offer up to 3x faster throughput and 10x more transactions per second than traditional S3-based Iceberg setups.
• Operational Efficiency: Built-in table maintenance including compaction, snapshotting, and metadata optimization reduces hands-on effort and keeps performance high as data scales.

2. Centralized, Enterprise-Grade Governance

• Fine-Grained Permissions: Lake Formation allows secure access at granular levels, crucial for compliance and safe data collaboration.
• Metadata Management: Automated cataloging and Glue Data Catalog integration make discovery and lineage tracking seamless.

3. Openness and Interoperability

• Open Formats: Data is stored in Apache Iceberg-compliant formats like Parquet, allowing seamless querying across AWS services (Athena, EMR, Redshift) and third-party engines (Spark, Flink, Trino, DuckDB).
• Resilience to Change: Iceberg enables schema evolution and time travel, essential for reliable analytics and adaptable data models.

4. Streamlined Operations from Ingest to Insight

• Automated Pipelines: Step Functions and Glue work together to automate ingestion, cleansing, and transformation with minimal manual intervention.
• BI-Ready Outputs: Real-time data is easily visualized through QuickSight dashboards, enabling faster and more informed decision-making.

Why S3 Tables with Apache Iceberg matter

Combining Amazon S3 Tables with Apache Iceberg significantly reduces the operational load of managing modern data lakes. Purpose-built for analytics workloads, S3 Tables deliver up to 3x faster query performance and 10x higher transaction throughput compared to self-managed Iceberg tables on S3. The fully managed infrastructure handles routine tasks such as compaction, snapshot cleanup, and metadata optimization automatically, ensuring consistent performance as data scales.

Scalability and governance go hand in hand. With centralized metadata and fine-grained access controls through AWS Lake Formation, enterprises can securely scale across thousands of tables and petabytes of data while meeting compliance requirements. Data is stored in Iceberg-compatible open formats, making it accessible across AWS-native services and third-party analytics engines.

This level of openness and interoperability ensures that the data lakehouse architecture can evolve alongside analytics and AI needs, future-proofing investments, and delivering consistent value to business teams.

Conclusion

Amazon S3 Tables, with Apache Iceberg and Lake Formation, form a powerful backbone for building a well-governed, future-ready data lakehouse. Organizations can accelerate their data transformation journey with reduced complexity, stronger governance, and faster insights—empowering both technical and business teams to turn raw data into real business value.

Ready to modernize your data platform into a governed, high-performance lakehouse? Connect with zeb to explore how this AWS-powered architecture can accelerate your analytics and AI initiatives.