Cloud Lone Star

AWS’s recent announcement of S3 Tables and S3 Metadata is making waves in the data lakehouse landscape. These new features offer robust tools for managing structured data in Amazon S3, enabling businesses to create tables and efficiently query data with metadata support while maintaining the scalability and flexibility of cloud storage.

But with these offerings, the question arises: Do you still need Apache Hudi? In this article, we’ll explore how AWS’s new features fit into the broader data lakehouse space and whether Hudi continues to play a vital role in modern data architectures.

What Are AWS S3 Tables and S3 Metadata?

Let’s first examine AWS’s latest capabilities:

• S3 Tables: This feature introduces a way to define tables directly over data stored in Amazon S3. It combines the raw power of a data lake with the structure typically found in data warehouses. Users can now organize data and perform structured queries on S3 directly, bringing more flexibility to big data workflows.
• S3 Metadata: Alongside S3 Tables, AWS offers enhanced metadata management for objects stored in S3. This includes schema details, partitioning information, and file formats, which streamline querying and indexing for large datasets.

Together, these features provide an integrated approach to structuring and querying data stored in S3, essentially laying the foundation for a data lakehouse architecture. This strategic move positions S3 as more than just a raw data repository—it’s now a powerful tool for structured analytics at scale.

What is Apache Hudi?

Apache Hudi is an open-source data management framework designed for cloud storage platforms like Amazon S3. It facilitates data lakehouse capabilities by offering advanced features such as:

• ACID Transactions: Supports transactional operations (inserts, updates, and deletes), crucial for maintaining the integrity of large datasets.
• Schema Evolution: Automatically handles schema changes, ensuring data consistency as business requirements evolve.
• Incremental Querying: Enables efficient querying of newly updated data, reducing the need to process entire datasets.
• Data Versioning & Time Travel: Allows historical data querying, enabling audits, change tracking, and maintaining historical records.

Do You Still Need Apache Hudi with AWS S3 Tables and Metadata?

While AWS’s new S3 Tables and S3 Metadata are powerful, they don’t necessarily replace the need for Apache Hudi in all scenarios. Here’s a breakdown of where each shines:

Where AWS S3 Tables and Metadata Excel:

• Simpler Table Management & Querying: If your use case involves organizing data in S3 and running basic queries, S3 Tables and Metadata provide an intuitive, lightweight solution with minimal overhead. They simplify data structuring and querying without requiring additional frameworks.
• Cost-Effectiveness: For organizations looking for a low-cost, easy-to-deploy solution, AWS’s new features may suffice. They enable structured data management directly in S3, avoiding the added complexity of integrating Hudi.

Where Apache Hudi Still Shines:

• ACID Transactions: For complex operations like updates, deletes, and upserts, Hudi provides robust, scalable transaction support that outperforms S3 Tables in high-volume environments.
• Advanced Data Management Features: Hudi’s capabilities, such as time travel, data versioning, and incremental querying, are essential for organizations with stringent data governance and audit requirements.
• Schema Evolution at Scale: If your data schema frequently changes, Hudi’s automatic schema management offers unmatched flexibility and efficiency.
• Incremental Data Processing: Hudi excels in processing only newly updated or inserted data, making it invaluable for ETL pipelines where speed and efficiency are critical.

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Do You Need Hudi?

AWS’s S3 Tables and S3 Metadata are transformative, offering businesses an accessible way to manage structured data directly in S3. These features are ideal for simpler use cases, where organizing data and performing basic analytics is sufficient. AWS’s innovations elevate Amazon S3’s role from raw storage to a more structured, lakehouse-oriented solution.

However, Apache Hudi remains essential for advanced data management. If your workload involves complex transactions, schema evolution, incremental processing, or historical querying, Hudi’s comprehensive feature set makes it a critical component of your architecture. As your data grows in size and complexity, Hudi’s value becomes even more apparent.