Modern data ecosystems demand a shift from traditional batch-based data lakes to real-time, reliable, and scalable lakehouse architectures. Apache Hudi has emerged as a foundational technology in this transition by offering transactional storage, incremental ingestion, and streaming write capabilities on top of distributed storage systems like Amazon S3, HDFS, and Azure Blob.

This blog explores Hudi’s role in modern data lake architectures, how it differs from legacy systems, and why it’s essential for building fast, queryable, and ACID-compliant cloud-native data platforms.

The Evolution from Data Lakes to Lakehouses

Traditional data lakes offer cheap storage but lack:

  • ACID transactions
  • Efficient updates/deletes
  • Schema evolution
  • Fresh, low-latency data

Modern data lakehouse architectures combine the scale and economics of data lakes with the transactional reliability of data warehouses.

Apache Hudi enables this by:

  • Bringing ACID guarantees to object storage
  • Supporting incremental processing and streaming ingestion
  • Optimizing for efficient upserts, deletes, and point-in-time queries

What is Apache Hudi?

Apache Hudi (Hadoop Upserts Deletes and Incrementals) is an open-source data lake framework that enables:

  • Real-time streaming ingestion into data lakes
  • Efficient data mutations (insert, update, delete)
  • Time travel and data versioning
  • Built-in metadata management, indexing, and compaction

Hudi supports multiple storage types:

  • Copy-on-Write (COW): For read-optimized access
  • Merge-on-Read (MOR): For write-heavy pipelines

Hudi’s Core Components in a Lakehouse

Component Function
Hudi Table Format Stores data with transactional semantics
Timeline Service Maintains commit history for version control
Write Client Handles inserts, updates, deletes, compaction
Metadata Table Optimizes partition and file listings
Hive Sync Syncs schema to Hive Metastore or Glue Data Catalog
Query Engines Supports Spark, Presto, Trino, Hive, Flink, Athena

Key Benefits in a Modern Lakehouse Stack

  1. ACID Transactions on S3/HDFS
    Guarantees atomic commits, rollback, and isolation for each write operation.

  2. Efficient Upserts & Deletes
    Enables real-time CDC ingestion, data corrections, and GDPR compliance workflows.

  3. Incremental Queries
    Hudi supports incremental views using commit timelines, reducing processing time.

  4. Time Travel & Data Versioning
    Query a Hudi table “as of” a particular commit time:

    SELECT * FROM sales_hudi
WHERE _hoodie_commit_time <= '20240410103000';

  5. Metadata Table for Fast Planning
    Avoids costly file listing in S3 by maintaining indexed partition and file metadata.

Hudi in a Typical Lakehouse Architecture

[Source Systems] → [Ingestion (Flink/Spark)] → [Hudi Table on S3]  
↘       ↘  
[Glue/Athena]   [Presto/Trino]  
↘       ↘  
[BI Tools]   [ML Pipelines]

Hudi acts as the storage abstraction layer, enabling efficient access for analytics, ML, and reporting tools.

Hudi vs Other Lakehouse Formats

Feature Apache Hudi Apache Iceberg Delta Lake
ACID Transactions ✅ Yes ✅ Yes ✅ Yes
Incremental Reads ✅ Native 🟡 Partial ✅ Native
Format Support Parquet, Avro Parquet, Avro, ORC Parquet
Metadata Indexing ✅ Metadata Table ✅ Catalog Integration ✅ Delta Log
Spark Compatibility ✅ High ✅ High ✅ High
Write Models COW / MOR Append-only COW / OPTIMIZE

Each system has its strengths, but Hudi is especially well-suited for streaming ingestion, record-level updates, and fast, scalable ETL workloads.

Common Hudi Use Cases

  • Change Data Capture (CDC): Ingest MySQL/Postgres CDC streams into S3
  • Streaming Data Lake Ingestion: Write from Kafka to S3 using Flink + Hudi
  • Regulatory Compliance: Delete and track PII for GDPR/CCPA
  • Data Warehouse Offloading: Migrate fact tables from RDBMS to Hudi-based lakes
  • Machine Learning Pipelines: Version feature sets and enable reproducible training

Best Practices for Production-Grade Hudi Deployment

  • Choose COW for fast queries, MOR for high-ingest workloads
  • Enable metadata table for file listing acceleration
  • Use record key + precombine key wisely for deduplication
  • Automate compaction and clustering to optimize file layout
  • Use Hive or Glue sync to expose tables to query engines
  • Monitor timeline health and commit frequency to avoid memory bloat

Conclusion

Apache Hudi plays a critical role in modern data lake architectures by transforming object storage into a fully capable lakehouse system. With its robust write performance, incremental processing, and transaction guarantees, Hudi empowers organizations to build real-time, scalable, and governed data platforms that serve both analytical and operational use cases.

As the demand for cloud-native data lakes grows, tools like Hudi will continue to lead the way in shaping the future of unified, open lakehouse ecosystems.