The modern data lake has evolved dramatically over the past few years. Gone are the days when data lakes were simply dumping grounds for Parquet files with fragile Hive metadata. Today's open table formats—Apache Iceberg, Delta Lake, and Apache Hudi—have transformed data lakes into reliable, performant analytical platforms with features previously reserved for traditional databases.
But which format should you choose? The answer isn't simple, and it depends on your specific use case, existing infrastructure, and organizational priorities. Let's dive deep into each format and help you make an informed decision.
What Problem Do Open Table Formats Solve?
Before comparing specific implementations, let's understand why these formats emerged. Traditional data lakes built on raw Parquet or ORC files suffered from several critical limitations:
- No ACID transactions: Concurrent writes could corrupt data, and readers might see partially written files
- Slow metadata operations: Operations like schema evolution or querying partition information required scanning thousands of files
- No time travel: Recovering from mistakes or auditing historical data states was nearly impossible
- Inefficient updates and deletes: Row-level modifications required rewriting entire partitions
- Schema evolution headaches: Adding or modifying columns across existing data was error-prone
Open table formats solve these problems by adding a metadata layer on top of your data files. This abstraction provides database-like features while keeping your data in open formats on cost-effective object storage.
Apache Iceberg: The Architect's Choice
Apache Iceberg, originally developed at Netflix and now a top-level Apache project, has gained significant momentum as the most architecture-forward of the three formats.
Architecture Highlights
Iceberg's architecture is built around a three-level metadata hierarchy: metadata files, manifest files, and manifest lists. This design enables extremely fast query planning, even with tables containing billions of files. The metadata layer tracks snapshots immutably, making time travel and rollback operations cheap and reliable.
One of Iceberg's standout features is hidden partitioning. Users query tables without knowing the partitioning scheme, and Iceberg automatically handles partition evolution. Change your partitioning strategy from daily to hourly? Iceberg handles queries across both partitioning schemes seamlessly.
Strengths
- True vendor neutrality: Iceberg was designed from the ground up to be engine-agnostic, with excellent support for Spark, Flink, Trino, Dremio, and more
- Sophisticated metadata management: The three-level hierarchy enables O(1) snapshot listing and efficient query planning
- Partition evolution: Change partitioning schemes without rewriting data or breaking queries
- Schema evolution done right: Track column IDs separately from names, preventing issues when columns are renamed or reordered
- Strong specification: Comprehensive spec means implementations are consistent across engines
Considerations
Iceberg's flexibility comes with complexity. Teams need to understand concepts like snapshot retention, metadata compaction, and orphan file cleanup. While these are solvable problems, they require operational maturity.
Delta Lake: The Databricks Powerhouse
Delta Lake, developed by Databricks and donated to the Linux Foundation, tightly integrates with the Spark ecosystem while offering some unique capabilities.
Architecture Highlights
Delta Lake uses a transaction log (the "Delta Log") stored as JSON files in the _delta_log directory. Each transaction appends a new JSON file, creating an ordered log of all table changes. This simple append-only design makes reasoning about table state straightforward.
Delta Lake's killer feature for many organizations is Delta Live Tables (DLT) and the broader Databricks integration. If you're heavily invested in the Databricks ecosystem, Delta Lake provides the smoothest experience.
Strengths
- Mature ecosystem: Delta Lake has been in production longer than competitors and has battle-tested implementations
- Excellent Databricks integration: Seamless experience with Unity Catalog, Delta Live Tables, and Databricks SQL
- Change Data Feed: Native CDC support makes streaming incremental changes straightforward
- Performance optimizations: Features like dynamic file pruning, data skipping, and Z-ordering deliver excellent query performance
- Simpler mental model: The transaction log approach is conceptually simpler than Iceberg's metadata hierarchy
Considerations
While Delta Lake has a vendor-neutral version (Delta.io), the reality is that it's optimized for Databricks. Support in other engines like Flink or Trino exists but lags behind Iceberg. If you're planning a multi-engine architecture, this ecosystem gap matters.
Delta Lake also lacks some of Iceberg's sophisticated features like hidden partitioning and true partition evolution. You can work around these limitations, but the solutions aren't as elegant.
Apache Hudi: The Streaming Specialist
Apache Hudi (Hadoop Upserts Deletes and Incrementals), originally created at Uber, was specifically designed for incremental data processing with streaming use cases in mind.
Architecture Highlights
Hudi's distinguishing feature is its storage types: Copy-on-Write (CoW) and Merge-on-Read (MoR). CoW is optimized for read-heavy workloads and writes complete Parquet files on each update. MoR uses a base Parquet file plus delta logs, optimizing for write performance at the cost of read-time merging.
This flexibility allows you to tune the read/write tradeoff based on your specific use case—something the other formats don't offer as directly.
Strengths
- Streaming-first design: Built from the ground up for incremental processing and CDC workloads
- Storage type flexibility: Choose between CoW and MoR based on your performance requirements
- Indexing capabilities: Bloom filters and other indexes can significantly accelerate point lookups
- Record-level changes: Track exactly which records changed for efficient incremental processing
- Clustering: Built-in data clustering improves query performance over time
Considerations
Hudi's streaming focus means additional complexity. The MoR storage type requires understanding compaction strategies, and the overall operational overhead is higher than Iceberg or Delta Lake.
Documentation and community resources, while improving, still lag behind the other two formats. You'll find fewer Stack Overflow answers and blog posts when troubleshooting issues.
Performance Comparison: Real-World Observations
Performance depends heavily on workload characteristics, but here are general patterns we've observed:
Read Performance: All three formats deliver comparable read performance for full table scans. Iceberg and Delta Lake edge ahead for metadata-heavy operations like partition pruning. Hudi's MoR can be slower for reads due to merge-on-read overhead, but CoW performs similarly to the others.
Write Performance: Hudi MoR wins for high-frequency updates, followed by Delta Lake. Iceberg's metadata overhead makes small, frequent commits slightly slower, though this is rarely a bottleneck in practice.
Update/Delete Performance: Hudi was built for this and generally performs best, especially with proper indexing. Delta Lake and Iceberg both handle updates well, with performance depending on file size and clustering strategies.
Query Planning: Iceberg's metadata architecture provides the fastest query planning for large tables with many partitions. The difference becomes noticeable when tables have millions of files.
Ecosystem and Tooling Support
Iceberg has the broadest engine support: excellent integration with Spark, Flink, Trino, Dremio, StarRocks, and others. AWS, Google Cloud, and Snowflake have all embraced Iceberg.
Delta Lake dominates in the Databricks ecosystem and has good Spark support elsewhere. Other engines (Flink, Trino) have added Delta support, but it's often a step behind Iceberg.
Hudi has strong Spark and Flink support, with growing support in other engines. The ecosystem is smaller but adequate for most streaming use cases.
Our Recommendations
After working with all three formats across various projects, here's our opinionated guidance:
Choose Iceberg if:
- You need true multi-engine support and want to avoid vendor lock-in
- Your architecture involves multiple query engines (Spark, Trino, Flink)
- You have large tables requiring sophisticated metadata management
- Partition evolution and hidden partitioning matter to your use cases
- You're building a new platform and want the most future-proof choice
Choose Delta Lake if:
- You're already invested in the Databricks ecosystem
- You want the most mature, battle-tested option
- Delta Live Tables or Unity Catalog are important to your architecture
- You primarily use Spark and don't need extensive multi-engine support
- You value comprehensive documentation and a large community
Choose Hudi if:
- Your primary use case is streaming CDC with frequent updates
- You need fine-grained control over read/write performance tradeoffs
- Record-level change tracking is critical to your architecture
- You're comfortable with higher operational complexity
- You have significant Uber-like streaming workloads with minute-level freshness requirements
The Future Landscape
The open table format space continues to evolve rapidly. Iceberg has tremendous momentum and appears to be emerging as the industry standard, with major cloud providers standardizing on it. Delta Lake's UniForm feature now allows tables to be read as Iceberg, acknowledging this shift while preserving Databricks' ecosystem advantages.
Meanwhile, Hudi continues to excel in its streaming niche, and recent developments suggest the formats may become more interoperable over time.
The good news? You can't make a catastrophically wrong choice here. All three formats are production-ready, actively maintained, and solve the fundamental problems of traditional data lakes. The decision comes down to ecosystem fit, team expertise, and specific feature requirements.
At DataBolt, we've successfully deployed all three formats for different clients. The key is understanding your requirements deeply and choosing the format that aligns with your broader data architecture rather than chasing the newest hype.