Apache Iceberg vs Delta Lake vs Hudi: Best Open Table Format for AI/ML Workloads

If you’re working with AI/ML workloads(like me)  and trying to figure out which data format to choose, this post is for you. Whether you’re a student, analyst, or engineer, knowing the differences between Apache Iceberg, Delta Lake, and Apache Hudi can save you a ton of headaches when it comes to performance, scalability, and real-time updates. By the end of this guide, you’ll have a solid grasp of core features and be able to pick the best open table format for AI/ML workloads. Let’s dive in!

Why Do We Need the Open Table Format for AI/ML Workloads?

Traditional data lakes have some limitations, and to address those challenges, three leading open table formats have been designed, I have added an architecture diagram for each format later in the post:

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

Key Benefits of These Formats

These formats address some of the most significant issues with traditional data lakes:

• Lack of Acid Transactions: Iceberg, Delta Lake and Hudi solve this ensuring reliability, concurrent reads and concurrent writes.
• No Past Data Tracking: Iceberg, Delta Lake, and Hudi enable this by reproducing past data states for debugging, ML training, or auditing.
• Data & Metadata Scalability: All three formats support real-time data scalability by file compaction.

Comparison Based On AI/ML Use Cases

Let’s take a look at the approaches of each format in key areas:

• Feature Stores: How well each format supports the data requirements for training ML models.
• Model Training: How well each format supports the data requirements for training ML models.
• Scalable ML pipelines: How well each format handles large-scale data processing.

Also read: What is Data Lakes? Step -by -Step Guide

What is the Apache Iceberg?

Apache Iceberg open table format has become an industry standard for managing data lakes and resolving the problems of the traditional data lake. It provides high analytics on large datasets.

In terms of feature stores, Apache Iceberg supports ACID transactions using snapshot isolation to ensure concurrent writes and reliability. Moreover, Iceberg allows schema changes without breaking existing queries meaning you don’t have to rewrite the datasets to make changes like used to do in traditional data lakes. Iceberg supports time travel using snapshots, allowing users to query older versions. Iceberg reduces the poor query performance by hidden partitioning and metadata indexing to speed up the query performance and it enhances data organization and access efficiency.

In terms of model training, Iceberg supports ML data requirements by optimizing fast data retrieval for faster model training by supporting time travel and using snapshot isolation ensuring that data remains consistent and does not get corrupted because of concurrent updates. It efficiently filters data by hidden partitioning to improve query speed and supports predicate pushdown, ensuring ML frameworks like Spark, PyTorch, and TensorFlow load data efficiently. Iceberg allows schema evolution without breaking queries supporting the evolving ML needs.

In terms of scalable ML pipelines, its compatibility with various processing engines, such as Apache Spark, Flink, Trino, and Presto, provides flexibility in building scalable ML pipelines. It supports faster pipeline execution ensuring shorter ML model training cycles. Iceberg supports incremental data processing so ML pipelines don’t have to reprocess the entire dataset; they only need to process changed or new data and that results in cost savings in a cloud environment. Iceberg supports ACID transactions ensuring safe concurrent writes and reliable ML data pipelines, avoiding data inconsistencies in distributed environments.

What is Apache Delta Lake?

Apache Delta Lake, developed by the creators of Apache Spark – Databricks, is an open-source data storage layer that integrates seamlessly with Spark for both reading and writing. It merges Apache Parquet data files with a sophisticated metadata log and has deep integrations with Spark

In terms of feature stores, Delta Lake performs ACID transactions and handles concurrency to ensure that writes, updates, and deletes do not result in corrupt data. To ensure enforceability and consistency within Delta Lake, metadata layers to track transactions. Furthermore, Delta Lake prevents entering bad data into the table by enforcing table restrictions and allowing for schema changes. Nevertheless, some schema alterations, such as dropping columns, require careful handling. Users are able to query previous versions of the data due to the time travel functionality enabled by the transaction log. Delta Lake optimizes query performance by utilizing its metadata and transaction logs. Importantly, Delta Lake enables real-time changes with the support of streaming writes. In addition, it solves cost and storage problems through real-time file compaction.

The Delta Lake maintains reliable and versioned training data with ACID transactions in model training. ML models use the time travel and rollback feature to train on historical snapshots which improves reproducibility and debugging. Using Z-ordering improves query performance and reduces I/O costs as it clusters similar data together. In addition, Delta Lake has been reported to improve read performance through partition pruning, metadata indexing, and Z-ordering. Finally, Delta Lake retains supporting schema changes without any effect on availability.

For scalable ML pipelines, the tight coupling of Delta Lake with Apache Spark makes it easier to integrate into existing ML workflows. New data is continuously ingested because it supports real-time streaming with Spark Structured Streaming, which enables quicker decision-making. Lastly, Delta Lake helps multiple ML teams to work on the same dataset simultaneously without corruption thanks to ACID transactions.

What is Apache Hudi?

Apache Hudi enhances the Apache Data Lake Stack with an open-sourced transactional storage layer that supports real-time analytics and incremental processing. Hudi allows data lakes to support incremental processing enabling slow batch processing to transform into near real-time analytics.

With regard to feature stores, Hudi has ACID transactions enabled, and it is possible to track events using the commit timeline and metadata layers. Thus, there is no chance of inconsistent data resulting from writes, updates, and deletes. Hudi allows some schema evolution, but certain schema changes such as dropping columns require care so as not to break existing queries. Hudi’s commit timeline also enables time travel and rollback functionality, which supports querying older versions and rolling back changes. In addition, Hudi’s query performance is improved through the use of several indexing techniques, including Bloom filters, and global and partition-level indexes. Hudi optimizes frequently updated tables using the Merge-on-Read (MoR) storage model. Hudi allows streaming writes but does not offer fully continuous streaming like Delta Lake’s Spark Structured Streaming. Instead, Hudi works with micro-batch or incremental batch modes with integrations to Apache Kafka, Flink, and Spark Structured Streaming.

Hudi is great for real-time machine learning implementations like fraud detection or recommendation systems because it enables real-time updates during model training. It lowers the compute cost because the system only has to load the altered data instead of reloading entire datasets. Merge-on-Read incremental queries are seamlessly managed. The flexible ingestion modes optimize Hudi’s batch and real-time ML training and can support multiple ML pipelines simultaneously.

With regards to scalable ML pipelines, Hudi was designed for streaming-first workloads; therefore it will be most appropriate for AI/ML use cases where data needs to be updated often as in ad-bidding systems. It has built-in small file management features to prevent performance bottlenecks. Hudi also allows efficient evolution over datasets by incorporating record-level updates and delete for both ML feature stores and training pipelines.

Apache Iceberg vs. Delta Lake vs. Hudi: Which Open Table Format you Should Choose for AI/ML Workloads?

If you’ve made it this far, we’ve learned about some of the important similarities and differences between Apache Iceberg, Delta Lake and Apache Hudi. 

The time has come to decide which format makes the most sense for your use case! My recommendation is guided by which scenario is most applicable: 

• Iceberg: Opt for Iceberg if you need efficient, large-scale batch processing with advanced metadata management, especially if working with historical data and requiring time travel.
• Delta Lake: Best for real-time, streaming AI/ML workloads where ACID transactions and incremental data processing are crucial.
• Hudi: Ideal if you need high-frequency updates in real-time streaming AI/ML workloads and prefer more fine-grained control over data.

Conclusion

If your primary concern is streaming data and real-time updates, then Delta Lake or Hudi may be your best choice in Open Table Format for AI/ML Workloads. However, if you need advanced data management, historical versioning, and batch processing optimization, Iceberg stands out. For use cases that require both streaming and batch processing with record-level data updates, Hudi is likely the best option.

Sumit Gupta

Sumit Gupta is a data science leader and published author with expertise in analytics, data modeling, and visualization. He is passionate about making data accessible and actionable, with experience at companies like Notion, Dropbox and Snowflake. Sumit also shares insights on SQL, dbt, and BI tools to help data professionals level up their skills on his LinkedIn and Instagram page