Breaking Down Data Barriers: Building a Unified Lakehouse with AWS

Enterprises continue to grapple with fragmented data systems, siloed information, and mounting governance challenges. As businesses look to harness data for both real-time decision-making and long-term innovation, there’s a growing need for a unified architecture that supports diverse analytics, from dashboards to machine learning.

At zeb, we work closely with enterprises to design scalable, secure data ecosystems tailored to these needs. One of the most effective approaches we’ve seen is building a unified lakehouse architecture using AWS. By combining Amazon S3 Tables, AWS Lake Formation, AWS Glue, and Amazon Athena, organizations can eliminate silos, implement centralized governance, and enable secure, scalable analytics for all users, whether they’re building business reports or training ML models.

This article outlines how to build a unified data lakehouse on AWS, the key services involved, and how this architecture supports hybrid analytics and enterprise-scale data management.

Unified architecture with AWS services

At the core of the lakehouse is Amazon S3 Tables, purpose-built storage designed specifically for analytics workloads. Unlike general-purpose S3 buckets, S3 Tables offer higher transaction throughput, automatic performance optimization, and support for Apache Iceberg format, making them ideal for storing tabular data like transactions, sensor streams, or log events. This format enables seamless schema evolution, time travel queries, and consistent data handling, all while maintaining S3’s renowned scalability and durability.

Layered on top, AWS Lake Formation simplifies and automates the setup of secure data lakes, allowing businesses to define fine-grained access controls and maintain consistent governance across teams and accounts.

AWS Glue adds automation for data integration, offering serverless ETL pipelines that clean, transform, and catalog data into a central metadata store. Meanwhile, Amazon Athena provides serverless SQL querying, giving analysts and data scientists instant access to insights without infrastructure management.

Together, these services create a cohesive ecosystem that supports both traditional BI and modern AI/ML use cases, within a governed, secure, and cost-efficient framework.

Key capabilities for enterprise scale

Centralized Data Governance
With AWS Lake Formation, businesses can enforce fine-grained access controls at the column, row, or table level, across different data sources and accounts. This ensures secure access to sensitive data without compromising usability or performance.

Optimized Storage
Amazon S3 Tables bring analytics-ready performance to your storage layer. By using Apache Iceberg and built-in maintenance automation, these tables allow organizations to scale their tabular data without worrying about manual tuning or complex governance setups. Whether you’re managing high-frequency transactions or streaming IoT feeds, S3 Tables provide a strong foundation for modern analytics use cases.

Serverless and Scalable
Amazon Athena and AWS Glue operate in a serverless model, reducing operational overhead and scaling automatically with demand. This architecture is ideal for organizations that need flexibility, speed, and cost-efficiency without managing infrastructure.

Hybrid Analytics Support
Whether you’re building dashboards in Amazon QuickSight or training ML models in SageMaker, the lakehouse provides a single data foundation for all workloads. The result: faster insights, more accurate models, and better business outcomes.

Cross-Account Data Sharing
Enterprises operating across multiple business units or partners can leverage AWS’s native support for secure, cross-account data sharing, eliminating duplication and enabling consistent access to governed datasets.

Real-world use cases

• Retail: Use serverless querying with Athena to analyze clickstream data, and combine it with sales and inventory data for better demand forecasting and personalized promotions.
• Financial Services: Build secure customer 360 profiles with Lake Formation and Glue, supporting regulatory compliance and AI-driven risk assessment.
• Manufacturing: Unify IoT sensor data and production logs in S3 Tables, and run predictive maintenance models directly on the lakehouse using Athena and SageMaker.

Cutting costs without compromising performance

The AWS lakehouse architecture eliminates the need for expensive, monolithic data warehouses and reduces costs through open formats, pay-as-you-go pricing, and Amazon S3 Tables’ optimized table buckets. These buckets are engineered for efficient storage and query execution, automatically compacting small files, managing snapshots, and removing unreferenced data to keep performance high and costs low.

By minimizing data movement and maximizing reuse through shared data catalogs and schemas, organizations can achieve operational efficiency while maintaining high performance and reliability.

At zeb, we help organizations adopt this architecture with minimal disruption and maximum impact, focusing on governance, scalability, and long-term agility.

Ready to simplify your data architecture?

With AWS, you can move from fragmented systems to a unified, AI-ready data foundation. Whether you’re scaling analytics, enhancing governance, or enabling cross-functional collaboration, the lakehouse architecture helps you do more, with less complexity.

zeb can help you get there, backed by proven expertise and tailored implementation strategies. With Amazon S3 Tables, AWS Lake Formation, AWS Glue, and Athena, you’re not just building a data platform, you’re building a foundation for innovation.