AWS S3 Tables: A New Contender in Lakehouse Solutions 2024

The announcement of AWS S3 Tables and SageMaker Lakehouse at re:Invent 2024 has generated buzz across the data engineering community. Positioned as a managed Apache Iceberg service, AWS’s latest offering promises faster queries, lower costs, and seamless integration with its ecosystem. With these advancements, AWS is directly challenging Databricks Delta Lake and other lakehouse leaders. But is this truly a disruptive innovation or simply the next chapter in the lakehouse evolution?

Let’s dive deep to separate the signal from the noise and explore what this means for the industry—and more importantly, for businesses already invested in Databricks Delta Lake.

🧩 Breaking Down S3 Tables

AWS’s S3 Tables, built on Apache Iceberg, offer features like:

ACID transactions
Time travel queries
Schema evolution

Built-in maintenance tools for compaction, snapshot management, and stale file cleanup simplify operations. Combined with integration across AWS services like Glue, Athena, and QuickSight, AWS is positioning itself as a serious contender.

🌟 Why Delta Lake Still Leads

While AWS brings impressive capabilities, Delta Lake continues to outshine with features that Iceberg and S3 Tables currently lack:

Streaming Capabilities:
Deep integration with Apache Spark Structured Streaming provides native batch and streaming support.
Advanced Data Mutations:
Robust APIs for MERGE, UPDATE, and DELETE make data corrections and historical updates seamless.
Schema Evolution:
Flexible schema management ensures compatibility with evolving business needs.
Unified Governance:
Unity Catalog offers comprehensive governance and collaboration features.
Proven Ecosystem:
Delta Lake’s optimized performance, broad toolset, and cross-cloud compatibility make it a trusted choice for large-scale data operations.

🔍 The AWS Catch

AWS’s pricing model can escalate quickly for high-frequency workloads, with costs including:

Storage: $0.0265 per GB/month—higher than standard S3 storage.
Compaction: $0.05 per GB processed.
Requests: $0.004 per 1,000 GETs and $0.005 per 1,000 PUTs.

For organizations with moderate usage, AWS estimates $35 per TB/month, but real-time workloads could see significant cost increases.

Additionally, while S3 Tables support Iceberg standards, their deep integration with AWS services may lead to vendor lock-in. In contrast, Delta Tables UniForm enables interoperability with multiple data formats, including Iceberg.

🚀 What Does This Mean for Databricks Users?

For businesses already using Databricks Delta Lake, there’s no need to panic. AWS’s entry into the lakehouse space validates the model but doesn’t overshadow Delta’s strengths. Delta Lake remains a superior choice for enterprises needing:

Real-time and batch processing capabilities.
Robust data mutation support.
A mature ecosystem with governance and collaboration tools.

At Techwards, we see this as an exciting time for the data industry. AWS’s announcement signals the growing importance of lakehouse architectures, but Delta Lake continues to lead as the most reliable and scalable foundation for modern data engineering.

🔮 Conclusion: A Bold Move, But Delta Stands Strong

AWS S3 Tables mark a significant step forward, particularly for AWS-centric organizations. However, Delta Lake’s maturity, advanced features, and scalability continue to make it the strategic choice for enterprises. The lakehouse wars are heating up, but for now, Delta Lake holds its ground as the industry leader.

What do you think? Is AWS rewriting the lakehouse rules, or does Databricks continue to dominate? Let us know your thoughts!

Stay tuned as we track these developments and help organizations navigate the evolving world of data engineering with scalable, secure lakehouse solutions.