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:
- No transactional guarantees: Multiple writers can corrupt data
- Poor metadata handling: Listing large directories is slow
- No schema evolution: Changing data structures is painful
- File management complexity: Small files degrade performance
- 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:
- Start with a pilot project: Choose a dataset that would benefit most from ACID properties
- Implement proper table design upfront:
- Plan for monitoring:
- Consider hybrid approaches:
- 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.
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