Apache Iceberg vs Delta Lake vs Apache Hudi: Which Table Format Should You Choose?

As organizations collect more data than ever before, traditional data lakes have become increasingly difficult to manage.

Storing files in cloud storage is easy, but ensuring data consistency, supporting updates, handling schema changes, and tracking versions is much more challenging.

This is where open table formats come in.

Among the most popular are Apache Iceberg, Delta Lake, and Apache Hudi. These technologies add a metadata layer to data lakes, making them more reliable, scalable, and easier to query.

While they solve many of the same problems, each was designed with different priorities and workloads in mind.

Choose Apache Iceberg for engine-agnostic analytics and large-scale data lakes, Delta Lake for organizations using Apache Spark and the Databricks ecosystem, and Apache Hudi for real-time data ingestion and incremental processing.

In this guide, we’ll compare Apache Iceberg, Delta Lake, and Apache Hudi to help you understand which one best fits your analytics and data engineering projects.

Why Do We Need Table Formats?

A traditional data lake often stores files like:

• Parquet
• ORC
• Avro

While these formats are efficient, they don’t provide features such as:

• ACID transactions
• Time travel
• Schema evolution
• Reliable updates
• Metadata management

Open table formats add these capabilities without replacing your existing storage.

What Is Apache Iceberg?

Apache Iceberg is an open table format originally developed at Netflix.

It was designed to improve reliability and performance for massive analytical datasets.

Key features include:

• ACID transactions
• Hidden partitioning
• Schema evolution
• Time travel
• Snapshot isolation
• Engine compatibility

Iceberg works with many popular analytics engines.

What Is Delta Lake?

Delta Lake is an open-source storage layer originally created by Databricks.

It extends Parquet-based data lakes by adding transactional capabilities.

Key features include:

• ACID transactions
• Schema enforcement
• Time travel
• Data versioning
• Streaming support
• Optimized Spark integration

Delta Lake is widely used in Spark-based analytics environments.

What Is Apache Hudi?

Apache Hudi (Hadoop Upserts Deletes and Incrementals) focuses on fast data ingestion and incremental processing.

It supports:

• Upserts
• Deletes
• Incremental queries
• Streaming ingestion
• Change data capture (CDC)

Hudi is especially useful when datasets change frequently.

Architecture

All three formats follow a similar structure:

Cloud Storage
      ↓
Parquet Files
      ↓
Metadata Layer
      ↓
Query Engine

The difference lies in how each format manages metadata, updates, and optimization.

ACID Transactions

All three formats support ACID transactions.

This means:

• Reliable writes
• Consistent reads
• Safe concurrent operations

Without ACID support, partially completed writes could leave datasets in an inconsistent state.

Time Travel

Time travel allows you to query previous versions of a dataset.

Example:

Current Table
      ↓
Previous Snapshot

This feature is valuable for:

• Auditing
• Debugging
• Reproducibility
• Data recovery

All three formats support some form of version history, though implementation details differ.

Schema Evolution

Business requirements change over time.

For example:

Old Schema
customer_id
name

becomes:

New Schema
customer_id
full_name
email

Modern table formats allow controlled schema evolution without recreating entire datasets.

Partitioning

Apache Iceberg

Uses hidden partitioning, allowing the system to manage partitions automatically.

This reduces common partitioning mistakes.

Delta Lake

Uses traditional partitioning while providing optimization features for improved query performance.

Apache Hudi

Supports partitioning with an emphasis on efficient incremental updates.

Streaming Support

If your data arrives continuously:

Events
   ↓
Streaming Pipeline
   ↓
Analytics

then streaming capabilities become important.

• Delta Lake integrates well with Spark Structured Streaming.
• Hudi is designed for streaming ingestion and change data capture.
• Iceberg increasingly supports streaming engines but primarily targets analytical workloads.

Query Engine Compatibility

Apache Iceberg

Supports a wide range of engines, including:

• Apache Spark
• Trino
• Presto
• Apache Flink
• Apache Hive
• Snowflake (through integrations)

Delta Lake

Works best with:

• Apache Spark
• Databricks

Support for additional engines continues to improve.

Apache Hudi

Commonly integrates with:

• Apache Spark
• Apache Flink
• Apache Hive

Performance

Performance depends on workload.

Iceberg

Excels at:

• Large analytical queries
• Complex scans
• Multi-engine environments

Delta Lake

Excels at:

• Spark workloads
• ETL pipelines
• Batch analytics

Hudi

Excels at:

• Frequent updates
• Incremental processing
• Near real-time ingestion

No single format is the fastest in every scenario.

Common Use Cases

Apache Iceberg

Ideal for:

• Enterprise data lakes
• Multi-engine analytics
• Large analytical datasets
• Cloud-native architectures

Delta Lake

Ideal for:

• Databricks users
• Spark-based ETL
• Machine learning pipelines
• Batch analytics

Apache Hudi

Ideal for:

• Change Data Capture (CDC)
• Streaming data
• Event processing
• Operational analytics

Feature Comparison

Feature	Apache Iceberg	Delta Lake	Apache Hudi
ACID Transactions	Yes	Yes	Yes
Time Travel	Yes	Yes	Yes
Schema Evolution	Excellent	Excellent	Good
Hidden Partitioning	Yes	No	No
Multi-Engine Support	Excellent	Good	Good
Spark Integration	Excellent	Excellent	Excellent
Streaming Workloads	Good	Excellent	Excellent
Incremental Processing	Good	Good	Excellent
Change Data Capture	Good	Good	Excellent

Which One Should You Choose?

Choose Apache Iceberg if you:

• Use multiple query engines
• Need strong interoperability
• Build large cloud-native data lakes
• Want flexible architecture

Choose Delta Lake if you:

• Already use Apache Spark
• Work heavily in Databricks
• Need reliable ETL pipelines
• Build machine learning workflows

Choose Apache Hudi if you:

• Process continuously changing data
• Need fast upserts and deletes
• Build real-time analytics systems
• Work extensively with CDC pipelines

Best Practices

Consider Your Ecosystem

Choose the table format that integrates best with your existing tools.

Prioritize Open Standards

Avoid unnecessary vendor lock-in when possible.

Understand Your Workload

Batch analytics, streaming, and incremental processing have different requirements.

Test Before Committing

Benchmark performance using your own datasets and query patterns.

Plan for Growth

Select a format that supports your long-term scalability and governance needs.

The Future of Open Table Formats

As organizations increasingly adopt cloud data lakes and lakehouse architectures, open table formats are becoming foundational technologies.

Apache Iceberg continues to gain momentum because of its engine independence and scalable metadata design.

Delta Lake remains a leading choice for Spark-first organizations, particularly those invested in the Databricks ecosystem.

Apache Hudi continues to excel in environments that require frequent updates and real-time ingestion.

Rather than competing directly, these formats address different priorities and often coexist across the modern data ecosystem.

Apache Iceberg, Delta Lake, and Apache Hudi all bring transactional reliability, schema evolution, and time travel to data lakes. The best choice depends on your infrastructure, workloads, and long-term architecture.

If flexibility across multiple query engines is your priority, Apache Iceberg is an excellent option. If your analytics platform is centered on Spark and Databricks, Delta Lake offers a mature and highly optimized experience. If your organization depends on continuous data ingestion and incremental processing, Apache Hudi is particularly well suited.

Understanding the strengths of each format will help you build more reliable, scalable, and future-ready analytics platforms.

FAQ