Apache Hudi vs. Apache Iceberg: 2025 Evaluation Guide on These Two Popular Open Table Formats

Updated February 26th, 2025

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Apache Hudi and Apache Iceberg are two of the four main table formats that data engineers consider when architecting a data lakehouse. The other two are Delta Lake and Apache Paimon.

Both Hudi and Iceberg address the limitations of the legacy table format of the Hadoop ecosystem, Apache Hive, but they were built to handle different workloads, reflected in their architecture and capabilities.
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In this article, we’ll compare Apache Hudi vs. Apache Iceberg to understand how they stack up against each other on the core technical specifications and capabilities. We’ll also see how a control plane for metadata is essential for having full visibility and context for all your data lakehouse assets, irrespective of the file or table formats used to store and manage them.

Table of Contents #

  1. Apache Hudi vs. Apache Iceberg: An overview
  2. Apache Hudi vs. Apache Iceberg: How do their features compare?
  3. Apache Hudi and Apache Iceberg ecosystems: The need for a metadata control plane
  4. Apache Hudi vs. Apache Iceberg: Wrapping up
  5. Apache Hudi vs. Apache Iceberg: Related reads

Apache Hudi vs. Apache Iceberg: An overview #

Apache Hudi and Apache Iceberg address Apache Hive’s shortcomings but cater to distinct workloads. Hudi is optimized for real-time streaming and transactional data lakes, while Iceberg is designed for large-scale analytics with advanced schema evolution, ACID compliance, and multi-engine compatibility.

To conduct an in-depth comparison of Apache Hudi vs. Apache Iceberg, it’s important to explore the history of open table formats. This evolution was driven by the need for scalable, efficient file management, leading to the emergence of Hudi and Iceberg.

A brief history of open table formats #


Apache Hadoop came into existence after Google’s MapReduce became popular in the mid-2000s. Organizations looking to store and crunch more data found the Hadoop ecosystem promising.

Apache Hive was created as a query engine and metadata layer on top of HDFS. Still, it quickly hit its scalability limits because it lacked query optimization using partition management, file pruning, etc.

As more use cases unfolded, Apache Hive fell short – it couldn’t support write-heavy workloads, especially real-time streaming workloads, without full file rewrites, which were costly. This triggered companies of scale, such as Uber, Netflix, and others, to chip in and create new table formats that were better suited for serving a wider range of use cases while also addressing the shortcomings of Apache Hive.

Let’s find out how Hudi and Iceberg solved some of these challenges.

Apache Hudi: A need for real-time ingestion and processing at Uber #


Apache Hudi was the first real alternative to Apache Hive. Uber’s data lake was hosted on Hadoop and used Hive Metastore as the catalog. One of the key use cases was to have updated user and trip data coming from the transactional databases, application queues, and streams.

Keeping the data up-to-date was challenging because even the smallest updates would trigger the scanning and rewriting of whole partitions. To support this, Hudi implemented two table types and update strategies – Copy On Write (COW) and Merge On Read (MOR).

Hudi also enabled Uber to perform near real-time ingestion, which helped various teams get quicker insights into what was happening with trips, arrival times, and user safety features, among other things.

Apache Iceberg: A need for transactions and efficient file management at Netflix #


While Hudi had already been developed by the time Netflix started facing issues with Apache Hive’s limitation, Netflix decided to take a different route. Key limitations included excessively slow query planning and expensive directory listing operations.

Because of its heavy read-and-write workloads, Netflix also needed transactions, which Hive lacked. Concurrent writes with consistency and atomicity weren’t an option with Hive.

To address this, Netflix developed Iceberg to offer snapshot isolation and atomic commits, ensuring a consistent view of data for both readers and concurrent writers.

High writes in Hive would have meant rewriting full partitions and, in many cases, full tables. That wasn’t an option for Netflix, so Iceberg was designed to support changes to partitions without rewriting the data. Iceberg also made queries much faster by implementing automatic partition pruning, i.e., hidden partitioning, to make queries run faster.

One of the other reasons for building Iceberg was also that Netflix wanted to create a table format compatible with multiple query engines, such asApache Spark, Apache Flink, and Trino.

Read more → Apache Iceberg at a glance

After going through the brief histories of both Hudi and Iceberg, let’s compare them based on their key differences.

Apache Hudi vs. Apache Iceberg: How do their features compare? #

Apache Hudi and Apache Iceberg were developed to overcome Apache Hive’s limitations, but they cater to different use cases.

Apache Hudi provides transactional capabilities with snapshot isolation, multi-version concurrency control, and indexing for efficient queries. However, it lacks partition evolution and full schema evolution support. It’s best suited for streaming, change data capture, and fast updates, making it ideal for real-time ingestion.

Iceberg, on the other hand, is designed for large-scale analytical workloads, supporting heavy write concurrency with ACID guarantees across multiple query engines. It offers full schema evolution, partition evolution, and robust query optimization, making it more suitable for long-term analytical workloads with point-in-time querying.

The following table compares Apache Hudi vs. Iceberg on the key criteria for a table format for lakehouse architectures.

Comparing on

Apache Hudi

Apache Iceberg

Function in a data lakehouse

Facilitates the creation of a transactional data lake or a data lakehouse with a focus on streaming ingestion

Helps create a data lakehouse with solid transaction support to enable high write concurrency with heavy analytical workloads

Support for transactions

Brings transactions to data lakes using snapshot isolation

Uses snapshot isolation to provide a consistent view of data to readers and concurrent writers

Concurrency control methods

Uses a range of concurrency control techniques, including multi-version concurrency control, optimistic concurrency control, and non-blocking concurrency control

Uses optimistic concurrency with atomic swaps of table metadata

Query planning and optimization

Implements multi-modal indexing, bloom filters, record indexes, secondary indexes, writer-side indexes, global and non-global indexes to support better lookups and aggregate queries

Leverages the ability to prune manifests and partitions based on the metadata maintained in the Iceberg catalog and metadata layer; gets rid of directory listings

Partition evolution

Doesn't support partition evolution as you have to rewrite the partitions if you want to change the partition scheme

Supports partition evolution by tracking partition metadata separately from their underlying data files

Schema evolution

Some operations like column additions are fully supported; however, removing, reordering, and renaming columns aren't natively supported

Supports full schema evolutions with the ability to add, drop, rename, and reorder columns without rewriting data

Point-in-time querying with time travel

Supports time travel using a combination of commits and savepoints; not designed to keep very long histories

Leverages snapshots to maintain full history of changes that allow you to query the state of data at any given point in time

Support for primary keys

Primary keys in Hudi are not enforceable, but defining them provides the basis for record-level indexing for better updates and deletes

Doesn't support primary keys as it is designed primarily for append-only analytical workloads

Write performance

Leverages Copy On Write (COW) and Merge On Read (MOR) strategies, which are optimized for small and incremental updates to data

Designed to handle large-batch updates without running into the small files problem

When is it best to use?

Hudi makes a good choice for real-time streaming ingestion or workloads that need small incremental updates

Iceberg makes a good choice when dealing with heavy analytical workloads with a need for point-in-time querying and schema evolution

Who uses it?

NerdWallet, Walmart, Zendesk, Notion already use Hudi

Airbnb, Apple, Adobe, and Salesforce use Iceberg

Irrespective of whether you choose either one or both the table formats with something like Apache XTable that makes them interoperable, you still need a fully-featured metadata platform to manage assets across file formats, table formats, storage systems, and so on.

The individual catalogs of Hudi and Iceberg maintain the metadata but can’t help you with business use cases involving data governance, lineage, quality, observability, and so on. For that, there’s a real need for a control plane of metadata that sits horizontally across all your data tools and technologies. That’s where Atlan comes in.

Apache Hudi and Apache Iceberg ecosystems: The need for a metadata control plane #

A metadata control plane like Atlan sits on top of your disparate data infrastructure, effectively stitching it together via cataloged metadata. As a result, data and business teams can find, trust, and govern AI-ready data. Such a setup centralizes data management and unites data producers and consumers throughout the organization.

Read more → What is a unified control plane for data?

Atlan integrates directly with Apache Polaris, which is one of Iceberg’s catalog implementations. This integration enables you to bring all of your Iceberg assets into Atlan and leverage capabilities, such as business glossary, data lineage, centralized data policies, and active data governance.

Atlan also integrates with Hudi via one of the Hudi cataloging options – AWS Glue Data Catalog, Google BigQuery, and Hive Metastore. There’s also an option to use Apache XTable to bring Hudi assets into Iceberg’s Polaris catalog and sync them with Atlan.

Bottom line – individual catalogs of various databases, storage engines, query engines, and table formats work well for their internal purposes but don’t provide real business value. For that, you need a metadata control plane like Atlan.

Apache Hudi vs. Apache Iceberg: Wrapping up #

As mentioned earlier, both Hudi and Iceberg address Apache Hive’s scalability and feature limitations – streaming ingestion and write-heavy analytical workloads. You can use either one of these or both in your data stack, especially with an interoperability tool like Apache XTable.

That still leaves the need for a centralized tool to manage all your data assets in one place for better overall governance – a control plane of metadata. To fulfill that gap, you can use Atlan and its various integrations with storage engines that support both Hudi and Iceberg or a direct integration with the Hudi catalog or the Iceberg catalog.

For more information on these integrations, check out the Atlan + Polaris integration or head over to Atlan’s official documentation for connectors.

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