Achieving Real-Time Data Processing with Apache Hudi in the Medallion Architecture

Introduction

In the era of big data, stream data processing has become critical for real-time analytics and decision-making. Traditional batch processing frameworks often struggle with the challenges of handling large-scale, dynamic datasets, leading to inefficiencies in data consistency, concurrency control, and query performance. The Medallion architecture, which divides data into Bronze, Silver, and Gold layers, provides a structured approach to data processing. However, its limitations—such as frequent full table scans and manual data management—have prompted the need for advanced solutions. Apache Hudi addresses these challenges by introducing a robust framework for incremental data processing, enabling seamless integration with the Medallion architecture.

Core Concepts of the Medallion Architecture

The Medallion architecture is a three-tiered data processing model designed to streamline data transformation and analysis:

• Bronze Layer: Stores raw, unprocessed data with duplicates and change logs.
• Silver Layer: Executes data cleaning, validation, and transformation operations.
• Gold Layer: Aggregates and joins Silver layer data to generate analytical results.

Traditional implementations face significant hurdles, including the need for frequent full table scans, manual consistency management, and scalability issues as data volumes grow. These challenges highlight the necessity for a more efficient solution.

Apache Hudi: A Modern Solution for Stream Data Processing

Apache Hudi is an open-source framework developed by the Apache Foundation, designed to address the limitations of traditional data lake architectures. It introduces several key features that enhance the efficiency of stream data processing within the Medallion architecture:

Key Features of Apache Hudi

1. Automated Table Services: Hudi automates file compaction, cleaning, clustering, and index management, reducing manual intervention.
2. Incremental Processing Framework: Supports record-level updates and merges, eliminating redundant processing of unchanged data.
3. Record-Level Indexing: Accelerates data localization and change tracking, minimizing the need for full table scans.

Platform Architecture

Hudi's architecture is divided into three layers:

• Data Lake Layer: Stores raw data in formats like Parquet or Avro.
• Transaction Layer: Manages file slices, base files, and log files to track changes.
• Execution Layer: Integrates with query engines such as Athena and Trino for open data access.

File Layout and Mechanisms

Hudi's file structure includes:

• Base Files: Store data snapshots generated during commits or merges.
• Log Files: Record insertions and updates after base files are created (in Algebird format).
• Timeline: Tracks operations like compaction and merging with timestamps and metadata.
• Metadata Table: Stores partition information, column statistics, and other metadata to optimize queries.

Concurrency control mechanisms, such as Multi-Version Concurrency Control (MVCC) and Bloom Indexes, ensure efficient data management in distributed environments.

Incremental Processing and CDC Capabilities

Hudi's incremental processing framework enables efficient data flow through the Medallion architecture:

1. Data Ingestion: Uses Hudi Streamer to pull changes from sources like Kafka or databases.
2. Bronze Layer: Stores raw events without transformation.
3. Silver Layer: Performs cleaning, transformation, and merging operations.
4. Gold Layer: Joins Silver layer data to generate analytical results.

Change Data Capture (CDC): Hudi supports CDC by capturing before/after change images, allowing downstream tables to be updated incrementally. This reduces the need for full table rewrites and improves real-time processing capabilities.

Real-World Applications and Performance Optimization

Hudi has been successfully adopted by enterprises such as Bite Dance, TikTok, and Walmart to handle massive datasets efficiently. Key performance optimizations include:

• Automated File Compaction and Cleaning: Reduces small file counts and improves query performance.
• Indexing and Metadata Management: Enhances query efficiency and reduces I/O overhead.
• Real-Time Analytics: Supports analysis responses from hourly to minute-level intervals.

Technical Integration and Ecosystem

Hudi seamlessly integrates with popular query engines like Athena and Trino, supporting common data formats such as Parquet and Avro. Developers can leverage Hudi Streamer for end-to-end incremental processing, with customizable transformation logic tailored to specific use cases.

Challenges and Considerations

While Hudi offers significant advantages, it also presents challenges such as complex configuration requirements and dependency on specific tools. Organizations must carefully evaluate their data workflows and infrastructure to maximize Hudi's benefits.

Conclusion

Apache Hudi revolutionizes stream data processing within the Medallion architecture by addressing traditional limitations through automation, incremental updates, and advanced indexing. Its ability to handle large-scale datasets efficiently makes it an ideal choice for real-time analytics and data lakes. By leveraging Hudi's features, organizations can achieve faster query performance, reduced operational overhead, and scalable data processing pipelines. For teams seeking to optimize their data workflows, adopting Hudi represents a strategic step toward modern data engineering practices.