Iceberg vs Delta Lake vs Hudi: The Open Table Format Showdown You Need to Read

If you're building a modern data platform in 2024, you've probably encountered the alphabet soup of open table formats: Iceberg, Delta Lake, and Hudi. These aren't just buzzwords—they represent a fundamental shift in how we architect data lakes, bringing database-like capabilities to object storage.

But here's the million-dollar question: which one should you choose? After implementing all three across different projects at DataBolt, I'm here to cut through the marketing noise and give you the practical insights you need.

Why Open Table Formats Matter

Before we dive into the comparison, let's establish why these formats exist. Traditional data lakes built on raw Parquet or ORC files have significant limitations:

• No ACID guarantees: Concurrent writes can corrupt your data
• Expensive metadata operations: Listing millions of files to understand your dataset is painfully slow
• Schema evolution headaches: Adding or changing columns requires rewriting entire datasets
• No time travel: Good luck recovering from that accidental DELETE operation

Open table formats solve these problems by adding a metadata layer on top of your data files. Think of them as bringing transactional database capabilities to your cheap and scalable object storage.

The Three Contenders

Apache Iceberg: The Clean-Sheet Design

Iceberg, originally developed at Netflix and now an Apache top-level project, was designed from scratch with cloud object stores in mind. Its architecture is elegant and purpose-built for the challenges of distributed data.

Key Architecture Insights:

Iceberg uses a three-level metadata structure: catalog → metadata files → manifest files → data files. This layered approach enables incredibly efficient metadata operations. When you query an Iceberg table, the engine reads a small JSON metadata file that points to manifest files, which in turn point to your actual data files.

What makes this powerful? Metadata operations are O(1) in terms of table size. Listing snapshots or reading schema doesn't require scanning through data files, regardless of whether you have 100 files or 100 million.

Where Iceberg Excels:

• True engine independence: First-class support for Spark, Flink, Trino, Dremio, and more
• Hidden partitioning: Users query without knowing partition schemes—the engine handles it
• Partition evolution: Change partition schemes without rewriting data
• Snapshot isolation: Multiple readers and writers without conflicts
• Metadata-only operations: Schema evolution and partition changes without touching data files

Gotchas:

Iceberg's flexibility comes with complexity. You need to choose a catalog implementation (Hive, Glue, Nessie, REST), and catalog choice significantly impacts your architecture. The ecosystem is rapidly maturing but still younger than Delta Lake in some areas.

Delta Lake: The Databricks Powerhouse

Delta Lake emerged from Databricks and has become deeply integrated with the Spark ecosystem. It's the most opinionated of the three formats, which can be both a strength and a limitation.

Key Architecture Insights:

Delta Lake uses a transaction log (the famous _delta_log directory) that records every change as JSON files. This append-only log serves as the single source of truth. To read a table's current state, you replay the log to build the file list.

This design is brilliantly simple but has scaling considerations. The log can grow large over time, which is why Delta Lake periodically creates checkpoint files (Parquet-formatted snapshots of the log state) to avoid reading thousands of tiny JSON files.

Where Delta Lake Excels:

• Databricks integration: If you're on Databricks, Delta Lake is the obvious choice with deep platform integration
• MERGE operations: CDC (Change Data Capture) workloads are a breeze with highly optimized MERGE
• Delta Sharing: Built-in protocol for sharing data across organizations without copying
• Ecosystem maturity: Extensive tooling, great documentation, and large community
• Performance optimizations: Z-ordering, bloom filters, and data skipping work exceptionally well

Gotchas:

Delta Lake historically had a Spark-first mindset. While Delta Universal Format (UniForm) now enables Iceberg/Hudi compatibility, and standalone readers exist, it's still most powerful within the Spark/Databricks ecosystem. If you need true multi-engine support, you'll need to evaluate whether UniForm meets your needs.

Apache Hudi: The Stream-Processing Specialist

Hudi (Hadoop Upserts Deletes and Incrementals), born at Uber, was purpose-built for streaming use cases and incremental data processing. It's the most feature-rich for update-heavy workloads but also the most complex.

Key Architecture Insights:

Hudi's distinguishing feature is its storage types: Copy-on-Write (CoW) and Merge-on-Read (MoR). CoW rewrites entire file groups on updates (read-optimized), while MoR writes delta logs and compacts asynchronously (write-optimized). This flexibility lets you optimize for your specific read/write patterns.

The timeline concept in Hudi tracks all actions (commits, compactions, cleans) with instant times, enabling sophisticated incremental processing patterns.

Where Hudi Excels:

• Streaming ingestion: Built-in support for handling late-arriving data and exactly-once semantics
• Incremental queries: Read only changed data between two commits—powerful for ETL pipelines
• Record-level operations: Upserts and deletes based on primary keys with impressive performance
• Indexing strategies: Multiple indexing options (bloom, simple, HBase) for different use cases
• Incremental processing: Native support for consuming tables as streams

Gotchas:

Hudi's power comes with operational complexity. You need to understand compaction strategies, cleaning policies, and clustering. The learning curve is steeper, and troubleshooting requires deeper knowledge of its internals. Hudi also has stronger ties to the Spark ecosystem, though Flink support has improved significantly.

Head-to-Head Comparison

Performance

Performance depends heavily on your workload. For large-scale analytics with fewer updates, all three perform similarly. For streaming ingestion with frequent upserts, Hudi's MoR tables often edge ahead. Delta Lake's optimizations (Z-ordering, liquid clustering) shine in Databricks. Iceberg's metadata design makes it exceptionally fast for metadata operations and partition pruning.

Ecosystem Support

Iceberg wins on true multi-engine support. It's designed to work identically across Spark, Flink, Trino, Dremio, StarRocks, and more. Delta Lake is Spark-first but expanding. Hudi primarily targets Spark and Flink.

Feature Completeness

All three support the basics: ACID transactions, time travel, schema evolution, and partition evolution. Hudi has the most sophisticated streaming features. Delta Lake has excellent CDC and sharing capabilities. Iceberg has the cleanest metadata operations and hidden partitioning.

Making Your Choice: A Decision Framework

Choose Apache Iceberg if:

• You need true multi-engine support and want to avoid vendor lock-in
• Your architecture uses multiple query engines (Spark + Trino + Flink)
• You value clean, forward-looking architecture
• Metadata operations and partition evolution are critical
• You're building on AWS (strong Glue integration) or want catalog flexibility

Choose Delta Lake if:

• You're using Databricks or planning to
• Your team is Spark-focused and values ecosystem maturity
• You need proven performance at massive scale (proven at thousands of companies)
• Data sharing across organizations is a requirement
• You want the most extensive documentation and community resources

Choose Apache Hudi if:

• Streaming ingestion and CDC are your primary use cases
• You need sophisticated upsert/delete performance on record-level updates
• Incremental processing patterns are central to your architecture
• You have the team expertise to manage operational complexity
• Your workload is update-heavy rather than append-mostly

The Emerging Reality: Interoperability

Here's something important that often gets missed in these comparisons: the formats are converging toward interoperability. Delta UniForm can write Delta and expose as Iceberg. Hudi is working on multi-format support. Cloud data warehouses are adding support for reading multiple formats.

This means your choice today isn't necessarily permanent. Start with what makes sense for your current stack and requirements, knowing you have migration paths if needed.

Final Thoughts

There's no universal winner in the Iceberg vs Delta Lake vs Hudi debate. After implementing production systems with all three, I've found that context matters enormously.

At DataBolt, we generally recommend Iceberg for new projects prioritizing flexibility and multi-engine support, Delta Lake for Databricks-centric architectures, and Hudi when streaming use cases with complex update patterns dominate.

The good news? All three are production-ready, actively developed, and represent a massive improvement over traditional data lake architectures. Your choice should be driven by your specific requirements, existing stack, and team expertise—not by which format has the most Twitter buzz this week.

The open table format revolution is here to stay. Pick the format that aligns with your needs, invest in understanding it deeply, and focus on building data systems that deliver value. That's what matters.