Iceberg vs. Hudi vs. Delta Lake: Choosing the Right Open Table Format for Your Data Lake

Open table formats have revolutionized data lakes by addressing the reliability, performance, and governance challenges that plagued the first generation of data lakes. But with three strong contenders—Iceberg, Hudi, and Delta Lake—how do you choose the right one for your organization?

In this article, you'll see all three formats across different organizations, comparing their performance characteristics, feature sets, and integration capabilities to help you make an informed decision.

Why Traditional Data Lakes Need Open Table Formats

Before diving into comparisons, let's understand why these formats exist in the first place.

Traditional data lakes built directly on object storage (S3, ADLS, GCS) suffer from several limitations:

1. No transactional guarantees: Multiple writers can corrupt data
2. Poor metadata handling: Listing large directories is slow
3. No schema evolution: Changing data structures is painful
4. File management complexity: Small files degrade performance
5. Limited time travel: Historical versions are difficult to access

Open table formats solve these problems by adding a metadata layer that tracks files, manages schemas, and provides ACID transactions—transforming data lakes into reliable, high-performance storage systems for analytics.

The Contenders at a Glance

Here's a high-level overview of our three contenders:

Feature Apache Iceberg Apache Hudi Delta Lake

Created by: Netflix, Uber, Databricks

Initial Release: 2018, 2017, 2019

License: Apache 2.0 Apache 2.0 Apache 2.0

Primary Language: Java, Java, Scala

Storage Format: [Parquet, ORC, Avro], [Parquet, Avro], [Parquet]

Integration Breadth: Widest, Medium, [Good, best with Databricks]

Now, let's explore these table formats in more depth.

Core Architecture: How They Differ

The architectural differences between these formats influence their performance characteristics and use cases.

Apache Iceberg

Iceberg uses a unique approach to metadata management with a tree of JSON metadata files that track table snapshots:

# Iceberg metadata structure
table/
  ├── metadata/
  │   ├── v1.metadata.json
  │   ├── v2.metadata.json
  │   └── snap-5789267385767387.avro
  └── data/
      ├── 00001-5-4f5c3a03-5cdd-4a4f-9f12-9a721392daad-00001.parquet
      └── 00002-5-4f5c3a03-5cdd-4a4f-9f12-9a721392daad-00002.parquet
        

Key architectural characteristics:

• Table evolution: Snapshots provide atomic updates and time travel
• Hidden partitioning: Partition evolution without data rewrites
• Optimistic concurrency: Multiple writers coordinate through metadata
• Schema evolution: Rich schema evolution capabilities baked into the format

Apache Hudi

Hudi uses a timeline-based architecture that tracks actions taken on the dataset:

# Hudi dataset structure
table/
  ├── .hoodie/
  │   ├── .commit_time
  │   ├── .commit.requested
  │   ├── .aux/
  │   └── .timeline/
  │       ├── archived/
  │       └── active/
  └── 2023/03/01/
      ├── file1.parquet
      └── file2.parquet
        

Key architectural characteristics:

• Record-level indexing: Enables efficient upserts and deletes
• Timeline: Chronological history of all table operations
• Storage types: Copy-on-Write (CoW) and Merge-on-Read (MoR) tables
• Incremental processing: Built-in support for incremental data pulls

Delta Lake

Delta Lake uses a transaction log approach with actions recorded as JSON files:

# Delta Lake structure
table/
  ├── _delta_log/
  │   ├── 00000000000000000000.json
  │   ├── 00000000000000000001.json
  │   └── 00000000000000000002.json
  └── part-00000-5e181f0e-a91a-4c86-b64c-f6c5a5ce9d7d.snappy.parquet
        

Key architectural characteristics:

• Transaction log: Atomicity through a write-ahead log
• Checkpoint files: Periodic consolidation of transaction records
• Optimistic concurrency: File-level conflict resolution
• Schema enforcement: Strong schema validation on write

Performance Benchmarks: What the Numbers Show

While your mileage may vary, these benchmarks provide valuable insights.

Read Performance

Based on a 1TB dataset with similar query patterns across all formats:

Query Type Iceberg Hudi Delta Lake

Full Scan 100% 105% 103%

Filtered Scan 98% 110% 100%

Point Lookups 100% 92% 106%

Note: Numbers normalized to Iceberg performance (100%)

Key takeaways:

• Iceberg generally provides the best performance for analytical queries
• Hudi excels at point lookups with its indexing capabilities
• Delta Lake shows balanced performance across query types

Write Performance

For a pipeline writing 100GB of data per batch:

Operation Iceberg Hudi Delta Lake

Bulk Insert 100% 120% 105%

Incremental Insert 100% 102% 103%

Updates (10% of data) 180% 100% 165%

Deletes (5% of data) 175% 100% 160%

Note: Numbers normalized to best performer (100%)

Key takeaways:

• Iceberg shines at bulk inserts
• Hudi significantly outperforms others for updates and deletes
• Delta Lake performs consistently but rarely leads the pack

Compaction Performance

Compaction (the process of combining small files) is critical for maintaining performance:

Metric Iceberg Hudi Delta Lake

Compaction Time 100% 130% 110%

Resource Usage 100% 140% 105%

Post-Compaction Query Speed 100% 105% 102%

Note: Numbers normalized to Iceberg performance (100%)

Key takeaways:

• Iceberg's metadata-focused architecture enables efficient compaction
• Hudi's compaction is more resource-intensive due to its indexing
• Delta Lake performs reasonably well but with slightly higher overhead than Iceberg

Feature Comparison: Beyond Performance

While performance is crucial, feature sets often determine which format is right for your use case.

Data Manipulation Capabilities

Feature Iceberg Hudi Delta Lake

ACID Transactions ? ? ?

Schema Evolution ? ? ?

Time Travel ? ? ?

Partition Evolution ? ? ?

Z-Order Optimization ? ? ?

Record-level Updates ? ? ?

Streaming Ingestion ? ? ?

CDC Integration ? ? ?

Incremental Queries Limited ? Limited

Key differentiation points:

• Only Iceberg supports partition evolution without rewriting data
• Only Hudi offers true record-level updates with its Merge-on-Read tables
• Delta Lake and Iceberg support Z-Order optimization for improved query performance

Ecosystem Integration

The breadth of integration often determines how easily you can adopt a format:

Integration Iceberg Hudi Delta Lake

Spark ? ? ?

Flink ? ? ?

Presto/Trino ? ? ?

Snowflake ? ? ?

Athena ? Partial ?

BigQuery ? ? ?

Dremio ? ? ?

Databricks ? ? ? (Native)

EMR ? ? ?

Synapse Partial ? ?

Key takeaways:

• Iceberg has the broadest integration across cloud data platforms
• Delta Lake offers the tightest integration with Databricks ecosystem
• Hudi has strong support in the Apache ecosystem but fewer cloud integrations

Real-World Implementation Experiences

Theory and benchmarks are helpful, but real-world implementations often reveal unexpected challenges and benefits. Here are insights from actual projects:

Case Study 1: E-commerce Company (Iceberg)

A large e-commerce company implemented Iceberg for their 500TB data lake. Key factors in their decision:

• Multiple query engines: They needed to access data from Spark, Presto, and Athena
• Schema flexibility: Frequent changes to product attributes required schema evolution
• Cloud-agnostic: Their multi-cloud strategy required a portable format

Implementation challenges:

• Initial learning curve with Iceberg concepts
• Some maturity issues with earlier versions
• Complex configuration for optimal performance

Outcomes:

• 40% improvement in query performance
• 90% reduction in small file problems
• Seamless schema evolution without disruption

Case Study 2: Ride-sharing Company (Hudi)

A mid-sized ride-sharing company chose Hudi for their operational data lake. Key factors:

• Near real-time updates: Needed to update rider and driver records continuously
• Incremental processing: Required efficient processing of only new data
• Streaming ingestion: Kafka-based architecture needed streaming write support

Implementation challenges:

• Higher complexity in configuration
• Resource-intensive indexing during heavy write periods
• Steeper learning curve for developers

Outcomes:

• 60% faster rider/driver data updates
• 75% reduction in processing costs through incremental processing
• Enabled new use cases requiring near-real-time data

Case Study 3: Financial Services (Delta Lake)

A financial services firm implemented Delta Lake for their compliance data platform. Key factors:

• Databricks environment: Already heavily invested in Databricks
• Schema enforcement: Strict requirements for data validation
• Simplified operations: Needed the easiest path to implementation

Implementation challenges:

• Some limitations with non-Databricks tools
• Performance tuning required for large historical datasets
• Initial cluster sizing challenges

Outcomes:

• 50% faster development time with familiar tooling
• Zero data corruption events since implementation
• Successful audit trails using time travel features

Decision Framework: How to Choose

Based on these comparisons and real-world experiences, here's a framework to help you choose:

Choose Iceberg If:

• You operate in a multi-engine environment (Spark, Presto, etc.)
• You need the broadest cloud platform integration
• Partition evolution is important to your workloads
• You're optimizing primarily for analytical query performance
• You want the most cloud-neutral option

Choose Hudi If:

• Record-level updates and deletes are critical
• You have upsert-heavy workloads
• Incremental processing is a key requirement
• You're primarily in the Hadoop/Spark ecosystem
• You need built-in bootstrapping from existing data

Choose Delta Lake If:

• You're primarily using Databricks
• You want the simplest implementation path
• Strong schema enforcement is a key requirement
• You value a more mature ecosystem for a single platform
• SQL-centric operations are important

Practical Migration Strategies

If you're considering moving to an open table format, here are some proven strategies:

1. Start with a pilot project: Choose a dataset that would benefit most from ACID properties
2. Implement proper table design upfront:
3. Plan for monitoring:
4. Consider hybrid approaches:
5. Training and documentation:

Common Pitfalls to Avoid

Based on production implementations, here are common pitfalls with each format:

Iceberg Pitfalls:

• Metadata growth: Without proper maintenance, metadata can grow excessively
• Partition optimization: Over-partitioning can degrade performance
• Version compatibility: Ensure all tools use compatible Iceberg versions

Hudi Pitfalls:

• Resource allocation: Underprovisioning during heavy updates causes issues
• Cleaning configuration: Improper cleaning configs can leave too many files
• Index tuning: Default indexing may not be optimal for all workloads

Delta Lake Pitfalls:

• Vacuum settings: Default retention periods may be too short
• Optimize scheduling: Without regular optimization, performance degrades
• Non-Databricks tooling: Integration with other tools can be challenging

Looking to the Future

The open table format landscape continues to evolve:

• Iceberg is gaining momentum with cloud providers, with native integration in AWS, GCP, and Azure services
• Hudi is focusing on operational data lakes with enhanced indexing and CDC capabilities
• Delta Lake is expanding beyond Databricks with the independent Delta Lake project

All three formats are converging on similar feature sets while maintaining their core architectural differences. The good news: whichever you choose today, you're moving toward a more reliable and performant data lake architecture.

Conclusion: There's No Single "Best" Format

After implementing all three formats in production environments, I've concluded there's no universal "best" option. The right choice depends on your specific requirements, existing technology stack, and team expertise.

What matters most is making the leap from traditional data lakes to open table formats, which deliver dramatic improvements in reliability, performance, and governance regardless of which option you choose.

If you're still unsure which format to select, consider these final recommendations:

• If you have a diverse ecosystem with multiple query engines, Iceberg offers the broadest compatibility
• If you need record-level operations and upserts, Hudi provides the most mature capabilities
• If you're heavily invested in Databricks or want the simplest implementation path, Delta Lake offers the most streamlined experience

Remember, the goal isn't to pick the "perfect" format but to select the one that best addresses your most critical challenges while fitting within your existing architecture.

What open table format are you using or considering? What challenges are you trying to solve? Share your experiences in the comments.