Delta Lake vs. Iceberg vs. Hudi
/tldr: Comparing the three leading open-source table formats that define the Data Lakehouse architecture.
1. The Lakehouse Layer (Metadata & Transactions)
These formats transform raw files (like Parquet) in a cloud storage layer (Data Lake) into transactional, queryable tables. They achieve this by adding a **transaction log** or **metadata layer** that tracks exactly which physical files belong to a specific table version at any point in time.
Delta Lake
Pioneered by Databricks. Focused on simplicity, ease of use with Apache Spark, and performance optimization (Z-Ordering, Liquid Clustering). Uses a transaction log (JSON/Parquet) for all operations.
Apache Iceberg
Pioneered by Netflix. Focused on correctness, cross-engine compatibility (Spark, Flink, Trino, Snowflake), and eliminating data partitioning pitfalls. Uses a tiered metadata structure (Snapshot > Manifest List > Manifest).
Apache Hudi
Pioneered by Uber. Focused on efficient record-level updates and change data capture (CDC), acting as a data warehouse layer for streaming ingest. Offers built-in indexing features.
2. Feature Comparison Matrix
| Feature | Delta Lake | Apache Iceberg | Apache Hudi |
|---|---|---|---|
| ACID Transactions | Yes (Atomicity, Consistency, Isolation, Durability) using a global transaction log. | Yes. Snapshots provide transaction isolation. | Yes, via optimistic concurrency control (OCC). |
| Schema Evolution | Excellent (Add, Drop, Rename columns) with automated schema enforcement. | Excellent. Supports column renames, reorders, and promotes schema correctness. | Good. Supports backward-compatible schema changes. |
| Time Travel / Versioning | Yes. Query by timestamp or version number via the transaction log. | Yes. Query by snapshot ID or time travel pointer. Highly efficient due to metadata structure. | Yes. Query based on commit time or incremental queries. |
| Upsert & Merge Strategy | **Copy-on-Write (CoW)**: Rewrite entire files containing changed records. Low query latency, high write amplification. | **Copy-on-Write (CoW)** is standard. **Merge-on-Read (MoR)** requires external tools or specific engine support (less native). | **Copy-on-Write (CoW)** and **Merge-on-Read (MoR)** are both native and optimized. MoR offers low write amplification, but higher read latency. |
| Merge Performance (Updates) | Requires rewriting files (CoW). Efficient for batch updates. | Similar to Delta (CoW). Good, but not optimized for high-volume, continuous micro-updates. | Best for fine-grained updates (MoR strategy mixes Parquet and Avro files) ideal for CDC/streaming use cases. |
| Partition Evolution | Limited. Changing partition layout requires rewriting data. | **Excellent.** Can change partition strategy (e.g., from day to hour) without rewriting old data files. | Good, but changes usually require table restructuring. |
| Engine Compatibility | Strong with Spark, but less portable historically. Growing support for Flink, Trino, etc. | **Widest Compatibility.** Highly compatible across Spark, Flink, Trino, Presto, Impala, and Snowflake. | Strong with Spark and Flink. Good compatibility with major SQL engines. |
3. When to Choose Which Format
Choose Delta Lake If:
- You are primarily using **Databricks/Apache Spark**.
- You prioritize a simple, centralized architecture and easy setup.
- Your updates are typically in large, manageable batches (CoW is acceptable).
Choose Apache Iceberg If:
- You need **maximum engine compatibility** (e.g., querying from Spark, Flink, and Trino simultaneously).
- You want to future-proof your data layout (excellent Partition Evolution).
- You prioritize strict correctness and eliminating classic Hadoop partitioning pitfalls.
Choose Apache Hudi If:
- Your core requirement is **record-level update/delete performance** (CDC/streaming ETL).
- You need the flexibility of choosing between Copy-on-Write and Merge-on-Read natively.
- You are migrating from a streaming data pipeline with frequent small updates.
All three formats deliver ACID properties, but they differ significantly in their metadata handling, compatibility, and optimization for updates/merges.