Apache Hudi 1.0.0: First Major Release

• 1.0.0 Release

Introduction to Apache Hudi

Apache Hudi is an open-source data management framework that simplifies working with large datasets stored on distributed file systems like Hadoop and cloud object stores. Originally developed to address challenges in building incremental data pipelines, Hudi introduces the concept of self-managing data lakes, enabling efficient updates, deletes, and incremental consumption of data.

The tool targets data engineers, developers, and analysts working with high-volume data pipelines, particularly in ETL and data streaming workflows. Hudi’s ability to support real-time data ingestion and provide ACID guarantees positions it as a critical solution for managing evolving datasets in scalable environments.

Version 1.0.0 marks its first major release, solidifying the tool’s readiness for production-grade use while addressing long-standing community feedback.

Features and Use Cases

Core Features

1. Transactional Data Lakes
   Hudi ensures data consistency by enabling ACID transactions on data lakes, a critical feature for workflows involving frequent updates and deletes.
2. Incremental Data Processing
   Unlike traditional batch processing, Hudi allows for incremental ingestion and consumption of data, reducing processing overhead for updates and deletions.
3. Flexible Table Types
   Hudi supports two primary storage types:
   • Copy-on-Write (COW): Optimized for read-heavy workloads.
   • Merge-on-Read (MOR): Suitable for write-heavy and mixed workloads.
4. Indexing Capabilities
   Indexes improve query performance, allowing fast lookups for updated records.
5. Seamless Integration
   Hudi integrates natively with distributed compute engines such as Apache Spark, Apache Flink, and Presto, making it easy to implement in existing data architectures.

Real-World Use Cases

1. Log Data Management
   A media streaming company processes terabytes of user activity logs daily. By adopting Hudi, the company transitions from batch-based pipelines to incremental ingestion, significantly reducing processing time and costs.
2. Data Lakehouse Architectures
   Enterprises building lakehouse systems use Hudi to bridge the gap between analytical and transactional systems. Its compatibility with table formats like Hive and Iceberg makes it a versatile choice.
3. Change Data Capture (CDC)
   Retail organizations leveraging Hudi’s CDC capabilities achieve real-time inventory updates across stores, enhancing operational efficiency.

Pros and Cons

Strengths

• Performance Efficiency
  Incremental processing reduces data latency and resource utilization compared to traditional batch processing.
• Ease of Integration
  Tight integration with Spark and Flink simplifies adoption for teams already leveraging these tools.
• Community-Driven Development
  Hudi’s active Apache community ensures rapid innovation and continuous improvement.
• Scalability
  Its design supports scaling to petabyte-level datasets without compromising performance.

Limitations

• Complexity in Setup
  Initial configuration, particularly for MOR tables, can be challenging for teams new to the tool.
• Steep Learning Curve
  Understanding Hudi’s indexing mechanisms and optimizing configurations requires expertise.
• Limited Native Support
  While integration with compute engines is strong, native support for certain query engines may lag behind alternatives like Delta Lake.

Integration and Usability

Apache Hudi is designed with interoperability in mind. It integrates seamlessly with widely used data processing frameworks:

1. Apache Spark
   Spark provides built-in Hudi connectors, making it straightforward to write, query, and manage datasets.
2. Apache Flink
   Recent advancements improve Hudi’s support for real-time stream processing.
3. Presto/Trino
   Query engines like Presto allow Hudi tables to be queried with minimal setup, enhancing analytical capabilities.

Usability

From a developer’s perspective, Hudi offers a rich set of configurations for fine-tuning performance. While it excels in flexibility, setting up pipelines requires attention to detail, particularly for users unfamiliar with distributed systems. However, the latest release includes improved documentation and a more intuitive API, lowering barriers to entry for new adopters.

Final Thoughts

Apache Hudi 1.0.0 represents a significant milestone in the evolution of data management tools for modern ETL, data streaming, and data lake workflows. Its blend of ACID transactions, incremental processing, and scalability positions it as a powerful alternative to tools like Delta Lake and Apache Iceberg.

Organizations dealing with rapidly changing datasets or requiring real-time data consumption will find Hudi particularly beneficial. However, the tool’s complexity may require an upfront investment in learning and setup. For teams prepared to navigate these challenges, Hudi offers a robust and versatile solution for managing large-scale data lakes.

Last Releases

• 1.0.0 ReleaseDecember 11, 2024
  Apache Hudi 1.0.0 is a major milestone release of Apache Hudi. This release contains significant format changes and new exciting features as we will see below. Migration Guide We encourage… Read more: 1.0.0 Release
• 0.15.0 ReleaseJune 20, 2024
  Apache Hudi 0.15.0 release brings enhanced engine integration, new features, and improvements in several areas. These include Spark 3.5 and Scala 2.13 support, Flink 1.18 support, better Trino Hudi native… Read more: 0.15.0 Release
• 0.14.1 ReleaseJanuary 4, 2024
  Migration Guide​ This release (0.14.1) does not introduce any new table version, thus no migration is needed if you are on 0.14.0. If migrating from an older release, please check… Read more: 0.14.1 Release