How to Choose the Best Open Table Format for AI/ML Workloads?

This guide helps AI/ML professionals choose the right open table format (Apache Iceberg, Delta Lake, or Apache Hudi) for their workloads. It outlines the key advantages of these formats over traditional data lakes, focusing on performance, scalability, and real-time updates.

Table of Contents:

• Why Open Table Formats are Essential for AI/ML
  • Key Advantages
  • AI/ML Use Case Comparison
• Understanding Apache Iceberg
• Understanding Apache Delta Lake
• Understanding Apache Hudi
• Choosing the Right Format for Your AI/ML Needs
• Conclusion

Why Open Table Formats are Essential for AI/ML Workloads:

Traditional data lakes lack crucial features. These three open table formats address these limitations:

1. Apache Iceberg
2. Delta Lake
3. Apache Hudi

Key Advantages:

These formats overcome common data lake challenges:

• ACID Transactions: Guaranteed reliability with concurrent reads and writes.
• Historical Data Tracking: Reproducing past data states for debugging, ML training, and auditing.
• Scalable Data & Metadata: Real-time scalability through file compaction.

AI/ML Use Case Comparison:

The guide compares each format's suitability for:

• Feature Stores: Data requirements for training ML models.
• Model Training: Data requirements for training ML models.
• Scalable ML Pipelines: Handling large-scale data processing.

Apache Iceberg:

Iceberg is an industry-standard open table format offering high-performance analytics on massive datasets. It excels in:

• Feature Stores: ACID transactions with snapshot isolation for concurrent writes and schema evolution without disrupting queries. Time travel using snapshots enables querying older versions. Hidden partitioning and metadata indexing improve query performance.
• Model Training: Optimized fast data retrieval for faster model training via time travel and snapshot isolation. Efficient data filtering through hidden partitioning and predicate pushdown. Supports schema evolution.
• Scalable ML Pipelines: Compatibility with Spark, Flink, Trino, and Presto. Faster pipeline execution and incremental data processing for cost savings. ACID transactions ensure reliable pipelines.

Apache Delta Lake:

Developed by Databricks, Delta Lake integrates seamlessly with Spark. Its strengths lie in:

• Feature Stores: ACID transactions and concurrency control. Metadata layers track transactions, enforcing data integrity and schema changes. Time travel functionality allows querying past data versions. Optimized query performance through metadata and transaction logs. Supports real-time changes.
• Model Training: Reliable, versioned training data with ACID transactions. Time travel and rollback features improve reproducibility and debugging. Z-ordering improves query performance. Supports schema changes without impacting availability.
• Scalable ML Pipelines: Tight Spark integration simplifies ML workflow integration. Real-time streaming with Spark Structured Streaming enables faster decision-making. ACID transactions support multiple concurrent ML teams.

Apache Hudi:

Hudi enhances the Apache Data Lake Stack with a transactional storage layer for real-time analytics and incremental processing. Its key features are:

• Feature Stores: ACID transactions, event tracking through commit timelines and metadata layers. Schema evolution (with caveats). Time travel and rollback. Improved query performance through indexing techniques. Optimized frequently updated tables using Merge-on-Read (MoR). Supports streaming writes (micro-batch or incremental batch).
• Model Training: Real-time updates for applications like fraud detection. Lower compute costs due to incremental data loading. Seamless Merge-on-Read incremental queries. Flexible ingestion modes optimize batch and real-time ML training.
• Scalable ML Pipelines: Designed for streaming workloads. Built-in small file management. Efficient dataset evolution with record-level updates and deletes.

Comparison Table:

Feature	Iceberg	Delta Lake	Hudi
ACID Transactions	Yes	Yes	Yes
Schema Evolution	Yes	Yes	Yes
Time Travel & Versioning	Yes	Yes	Yes
Query Optimization	Yes (Best)	Yes	Yes
Real-time Streaming Support	No	Yes	Yes (Best)
Storage Optimization	Yes	Yes	Yes

Choosing the Right Format:

• Iceberg: Best for large-scale batch processing with advanced metadata management and time travel needs.
• Delta Lake: Ideal for real-time, streaming workloads requiring ACID transactions and incremental processing.
• Hudi: Best for high-frequency updates in real-time streaming and fine-grained data control.

Conclusion:

The optimal choice depends on your specific AI/ML workload requirements. Consider whether you prioritize streaming data, real-time updates, advanced data management, historical versioning, or batch processing optimization when making your decision.

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