How do open table formats compare?

Summary

• Apache Iceberg, Delta Lake, and Apache Hudi each bring ACID transactions, schema evolution, and time travel to cloud storage but differ in architecture and ideal workload fit.
• Key selection criteria include engine compatibility, batch versus streaming workload mix, partition evolution support, metadata overhead, and ecosystem maturity.
• The Databricks Data + AI Platform with Unity Catalog provides unified governance across Delta Lake, Iceberg, and Parquet, eliminating format lock-in while delivering warehouse-grade performance.

Comparing open table formats: a guide to choosing the right one for your data lakehouse

Choosing an open table format is one of the most consequential decisions in building a modern data platform. The format determines which query engines can access your data, how efficiently queries run, and how much operational overhead your team absorbs.
Data lakes traditionally store raw files but lack structure, metadata tracking, and transactional guarantees. Open table formats solve this by adding a transactional layer on top of those files, boosting data reliability on cloud storage.
The primary formats in use today are Apache Iceberg, Delta Lake, and Apache Hudi. Each has a distinct architecture and set of trade-offs worth understanding before you commit.

What is an open table format?

An open table format is an openly governed specification with two inseparable parts: table metadata and a transaction protocol. Together they turn files on cloud object storage into a database-like table. Key capabilities shared across all major formats include:

• ACID transactions: reliable concurrent reads and writes.
• Schema evolution: add, drop, or rename fields without breaking downstream consumers.
• Time travel: query data as it existed at a specific point in time.
• Row-level mutations: upserts, merges, and deletes on immutable storage.

How the three major open table formats compare

Each format originated from a different production need. Delta Lake emphasizes transaction-log reliability. Iceberg prioritizes engine-agnostic analytics at scale. Hudi targets efficient incremental processing.

Format	Origin	Core strength
Delta Lake	Databricks	Transaction-log-based, strong batch and stream unification
Apache Iceberg	Netflix	Snapshot- and manifest-driven, engine-agnostic, fast large-scale analytics
Apache Hudi	Uber	Built for upserts, deletes, and incremental processing with COW/MOR modes

All three store data in Apache Parquet files. Updates are versioned and rewritten using a copy-on-write pattern.

Why open formats matter for the lakehouse

Open table formats and lakehouse patterns are going mainstream. Major cloud vendors are standardizing on Iceberg and other open formats, signaling an industry shift toward lakehouse-style architectures.
Fragmented stacks create real problems. Separate ETL, warehouses, and BI tools duplicate work and definitions, slow decisions, and increase costs. According to McKinsey, fragmented data repositories consume between 15 and 20 percent of the average IT budget.
Proprietary stacks and closed formats restrict choice and block interoperability. Organizations looking to modernize can explore warehouse-to-lakehouse migration approaches to reduce this fragmentation.

Choosing a format: key decision criteria

When evaluating open table formats, consider these factors independently of any vendor:

• Engine compatibility: which query engines and processing frameworks does each format support?
• Workload mix: does your pipeline lean toward batch, streaming, or frequent upserts?
• Partition evolution: can you change partitioning schemes without rewriting data?
• Metadata overhead: how does each format manage metadata at scale?
• Community and ecosystem: how broad is adoption, and how active is development?

No single format is best for every scenario. Teams running heavy CDC pipelines may favor Hudi. Broad multi-engine analytics teams often gravitate toward Iceberg. Organizations deeply integrated with Spark may prefer Delta Lake.

How Databricks supports open table formats

The Databricks Data + AI Platform with Unity Catalog provides a single catalog for all data, managing Delta Lake, Apache Iceberg, and Parquet with one set of permissions, lineage, and business definitions. Open formats are first-class citizens, not bolt-ons.

• Single governance model: one set of permissions and lineage across all supported formats.
• No format lock-in: teams use the format that fits without sacrificing governance.
• Performance optimizations: Photon, Predictive IO, and Intelligent Workload Management deliver warehouse-grade performance on an open lakehouse foundation.

Interoperability with Apache xtable

Apache XTable enables conversion between open table formats by translating metadata without duplicating data. It is not a new format, it provides abstractions for cross-format interoperability.
This can reduce the pressure to standardize on a single format across an organization.

FAQs

What are the main open table formats available for data lakehouses?

The primary formats are Apache Iceberg, Delta Lake, and Apache Hudi. All are open source and designed to bring transactional capabilities to cloud object storage.

What are the key features and capabilities of Apache Iceberg?

Iceberg provides ACID transactions, schema evolution, hidden partitioning, and time travel on top of Parquet, ORC, and Avro. It works with Spark, Flink, Presto, and other engines.

What are the key features and capabilities of Delta Lake?

Delta Lake adds ACID transactions to Spark and big data workloads. Structured Streaming and batch jobs can read and write the same table, simplifying streaming data ingestion and reducing architectural complexity.

What are the key features and capabilities of Apache Hudi?

Hudi supports ACID transactions, incremental processing, and efficient indexing. It was designed at Uber for incremental updates over columnar data using Spark and Flink.

How do open table formats handle schema evolution and time travel?

Schema changes are versioned by the format, so consumers do not need coordinated updates. Time travel lets you access previous data versions without reprocessing files.

What factors should i consider when choosing an open table format for my data lakehouse?

Consider engine compatibility, streaming-versus-batch workload mix, partition evolution needs, and governance requirements. Unity Catalog on the Databricks Data + AI Platform lets teams use Delta Lake, Iceberg, and Parquet under a single governance model.

Build your lakehouse on open formats

Open table formats are the foundation of the modern data lakehouse. Choosing how to govern them matters as much as choosing the format itself.
The Databricks Data + AI Platform with Unity Catalog provides a single governance layer across Delta Lake, Apache Iceberg, and Parquet, so every user and tool works from the same trusted source. Explore the Databricks Data + AI Platform to get started.