Introduction
Modern data lakes power analytics, machine learning, and real-time processing across enterprises. However, traditional data lakes suffer from challenges like slow queries, lack of ACID transactions, and inefficient updates.
This is where open table formats like Apache Iceberg and Apache Hudi come into play. These formats provide database-like capabilities on data lakes, ensuring better data consistency, faster queries, and support for schema evolution.
But which one should you choose? Apache Iceberg or Apache Hudi? In this blog, we’ll explore their differences, use cases, performance comparisons, and best-fit scenarios to help you make an informed decision.
Understanding Apache Iceberg & Apache Hudi
What is Apache Iceberg?
Apache Iceberg is an open-source table format designed for large-scale batch analytics and data lakehouse architectures. Initially developed at Netflix, it provides:
✅ Hidden partitioning for optimized query performance
✅ ACID transactions and time travel
✅ Schema evolution without breaking queries
✅ Support for multiple compute engines like Apache Spark, Trino, Presto, and Flink
What is Apache Hudi?
Apache Hudi (Hadoop Upserts Deletes and Incrementals) is a streaming-first data lake format designed for real-time ingestion, Change Data Capture (CDC), and incremental processing. Originally developed at Uber, it offers:
✅ Fast upserts and deletes for near real-time data updates
✅ Incremental processing to reduce reprocessing overhead
✅ Two storage modes: Copy-on-Write (CoW) and Merge-on-Read (MoR)
✅ Strong support for streaming workloads using Kafka, Flink, and Spark Structured Streaming
Architectural Differences
| Feature | Apache Iceberg | Apache Hudi |
|---|---|---|
| Storage Format | Columnar (Parquet, ORC, Avro) | Columnar (Parquet, ORC, Avro) |
| Metadata Management | Snapshot-based (manifests and metadata trees) | Timeline-based (commit logs) |
| Partitioning | Hidden partitioning (no need to manually manage partitions) | Explicit partitioning |
| Schema Evolution | Supports adding, renaming, dropping, and reordering columns | Supports adding, updating, and deleting columns |
| ACID Transactions | Fully ACID-compliant with snapshot isolation | ACID transactions with optimistic concurrency control |
| Compaction | Lazy compaction (only rewrites when necessary) | Active compaction (required for Merge-on-Read) |
| Time Travel | Fully supported with snapshot-based rollbacks | Supported via commit history |
| Indexing | Uses metadata trees and manifest files | Uses bloom filters, column stats, and indexing |
Key Takeaways
- Apache Iceberg is better for batch analytics and large-scale queries due to its hidden partitioning and optimized metadata management.
- Apache Hudi is optimized for real-time ingestion and fast updates, making it a better fit for streaming and CDC workloads.
Performance Comparison
Read Performance
📌 Apache Iceberg performs better for large-scale batch queries due to hidden partitioning and efficient metadata pruning.
📌 Apache Hudi can have slower reads in Merge-on-Read (MoR) mode, as it requires merging base files and log files at query time.
Write Performance
📌 Apache Iceberg is optimized for batch writes, ensuring strong consistency but may be slower for real-time updates.
📌 Apache Hudi provides fast writes by using log files and incremental commits, especially in Merge-on-Read (MoR) mode.
Update & Delete Performance
📌 Apache Iceberg does not natively support row-level updates, requiring a full rewrite of affected data files.
📌 Apache Hudi is designed for fast updates and deletes, making it ideal for CDC and real-time applications.
Compaction Overhead
📌 Apache Iceberg does lazy compaction, reducing operational overhead.
📌 Apache Hudi requires frequent compaction in Merge-on-Read (MoR) mode, which can increase resource usage.
Ecosystem & Integration
| Feature | Apache Iceberg | Apache Hudi |
|---|---|---|
| Compute Engine | Spark, Trino, Presto, Flink, Hive | Spark, Flink, Hive |
| Cloud Storage | S3, ADLS, GCS, HDFS | S3, ADLS, GCS, HDFS |
| Streaming Support | Limited | Strong (Kafka, Flink, Spark Streaming) |
| Data Catalog Support | Hive Metastore, AWS Glue, Nessie | Hive Metastore, AWS Glue |
Key Takeaways
- Apache Iceberg is widely adopted in analytics platforms like Snowflake, Dremio, and AWS Athena.
- Apache Hudi is tightly integrated with streaming platforms like Kafka, AWS EMR, and Databricks.
Use Cases: When to Choose Iceberg or Hudi?
| Use Case | Best Choice | Why? |
|---|---|---|
| Batch ETL Processing | Iceberg | Optimized for large-scale analytics |
| Real-time Streaming & CDC | Hudi | Designed for fast ingestion and updates |
| Data Lakehouse (Trino, Snowflake) | Iceberg | Better query performance & metadata handling |
| Transactional Data in Data Lakes | Hudi | Provides efficient upserts & deletes |
| Time Travel & Data Versioning | Iceberg | Advanced snapshot-based rollback |
| Incremental Data Processing | Hudi | Supports incremental queries & CDC |
Key Takeaways
- Choose Apache Iceberg if you focus on batch analytics, scalability, and time travel.
- Choose Apache Hudi if you need real-time ingestion, fast updates, and streaming capabilities.
Final Thoughts: Iceberg or Hudi?
Both Apache Iceberg and Apache Hudi solve critical data lake challenges, but they are optimized for different workloads:
🚀 Choose Apache Iceberg if you need a scalable, reliable, and high-performance table format for batch analytics.
🚀 Choose Apache Hudi if your priority is real-time ingestion, CDC, and fast updates for transactional workloads.
With big data evolving rapidly, organizations must evaluate their performance, query needs, and streaming requirements before making a choice. By selecting the right table format, businesses can maximize data efficiency, reduce costs, and unlock the true potential of their data lakes.
📢 Which table format are you using? Let us know your thoughts in the comments! 🚀
No comments:
Post a Comment