What is Apache Hudi and what capabilities does it provide for Data Lakes?

Apache Hudi (Hadoop Upserts Deletes and Incrementals) is an open-source data management framework that brings stream processing capabilities to batch data pipelines. Developed at Uber and contributed to the Apache Software Foundation, Hudi enables efficient upserts, deletes, and incremental data processing on data lakes.

Hudi was specifically designed to handle change data capture (CDC) and near real-time analytics on massive datasets. It excels at scenarios requiring frequent updates to existing records—common in operational analytics, CDC ingestion, and late-arriving data corrections.

Core Capabilities of Apache Hudi:

1. Upsert Support: Hudi's primary strength is efficiently upserting (update + insert) billions of records. When ingesting CDC streams or incremental updates, Hudi merges changes with existing data without requiring full table rewrites. This is critical for maintaining dimension tables and fact tables with frequent updates.

// Scala/Spark upsert example
inputDF.write
  .format("hudi")
  .option("hoodie.table.name", "customers")
  .option("hoodie.datasource.write.operation", "upsert")
  .option("hoodie.datasource.write.recordkey.field", "customer_id")
  .option("hoodie.datasource.write.precombine.field", "update_ts")
  .mode("append")
  .save("/data/customers")

2. Incremental Queries: Hudi tracks which files changed since a given checkpoint, enabling incremental queries that only read new or modified data. This dramatically reduces processing time for downstream pipelines that need to process only changes since the last run.

3. Copy-on-Write vs Merge-on-Read: Hudi offers two table types with different trade-offs:

• Copy-on-Write (COW): Updates rewrite entire file groups, optimizing for query performance. Best for read-heavy workloads with fewer updates.
• Merge-on-Read (MOR): Updates are written to delta logs and merged during reads, optimizing for write performance. Best for write-heavy, near real-time workloads.

4. ACID Transactions: Hudi provides atomicity for batch and streaming writes using a timeline-based approach. Multiple writers can safely update different partitions simultaneously, with Hudi managing concurrency through optimistic locking.

5. Compaction and Cleaning: Hudi automatically manages small files through compaction processes and cleans old versions based on retention policies, preventing performance degradation over time.

6. Bootstrap Existing Tables: Hudi can bootstrap existing Parquet/ORC data lakes, adding Hudi metadata without rewriting data, enabling gradual migration to Hudi-managed tables.

Hudi is particularly popular in scenarios requiring real-time data lake updates, such as operational analytics, customer 360 platforms, and fraud detection systems. It integrates with Spark, Flink, Presto, Trino, and Hive.