As data lakes evolve into lakehouse architectures, choosing the right table format becomes crucial for performance, reliability, and flexibility. Among the top contenders are Apache Hudi, Delta Lake, and Apache Iceberg — each designed to bring ACID transactions, schema evolution, and time-travel capabilities to modern big data platforms.

In this blog, we provide a side-by-side comparison of Hudi, Delta Lake, and Iceberg to help you choose the best solution based on your workload, infrastructure, and integration needs.

What Are Lakehouse Table Formats?

Lakehouse formats bring database-like features to object storage (e.g., HDFS, S3, GCS):

  • ACID transactions
  • Efficient upserts and deletes
  • Time travel and versioning
  • Schema evolution
  • Streaming + batch ingestion
  • Partition and metadata management

These formats decouple compute and storage while enabling reliable and performant querying across Spark, Presto, Hive, Flink, Trino, and more.

Feature Comparison Table

Feature Apache Hudi Delta Lake Apache Iceberg
ACID Transactions Yes (MVCC) Yes (Optimistic) Yes (Snapshot-based)
Merge Support Yes Yes Yes
Streaming Ingestion ✅ Native ✅ Structured Streaming ✅ Flink, Spark
Time Travel ✅ Commit-based ✅ Version/Time-based ✅ Snapshot/Ref-based
Schema Evolution ✅ ✅ ✅ Advanced
Partition Evolution ❌ Static ❌ Static ✅ Supported
Table Format Type Log/Column Hybrid Column-based Column-based
Metadata Storage Timeline Logs (._hoodie) JSON (transaction log) Manifest & Metadata Files
Format Compatibility Parquet, Avro, ORC Parquet only Parquet, ORC, Avro
Multi-Engine Support Spark, Hive, Flink Spark (limited Trino) Spark, Flink, Trino, Presto
Lakehouse Integration Good Best with Databricks Best for Open Architectures
Community & Governance Apache Linux Foundation Apache

Apache Hudi Overview

Apache Hudi is best known for:

  • Streaming-first ingestion (e.g., Kafka to HDFS)
  • Upserts and incremental processing
  • Integration with Hive, Spark, Flink, and Presto
  • Strong ecosystem around CDC (Change Data Capture)

Use cases:

  • Real-time ETL
  • CDC pipelines
  • Partitioned log data with near real-time availability

Hudi table types:

  • Copy-On-Write (CoW) for query-optimized reads
  • Merge-On-Read (MoR) for streaming ingest and low-latency writes

Delta Lake Overview

Delta Lake, created by Databricks, adds ACID and versioning to Parquet-based data lakes.

Key features:

  • Tight Spark integration
  • Simple schema evolution
  • Scalable metadata handling via Delta Log
  • Best performance with Databricks Runtime

Use cases:

  • BI workloads on Spark
  • Time travel and rollback
  • ML pipelines requiring atomic writes

Limitations:

  • Parquet-only
  • Vendor lock-in with full features on Databricks
  • Limited native support on Hive/Flink

Apache Iceberg Overview

Apache Iceberg is designed for large-scale, long-term data lake management with full schema and partition evolution.

Highlights:

  • Supports hidden partitioning
  • Highly scalable metadata tree (manifests)
  • First-class support in Flink, Trino, Hive, Spark
  • Built for performance and open formats

Use cases:

  • Large multi-petabyte data lakes
  • Long-lifecycle tables with evolving schemas
  • Lakehouse systems with Trino/Flink/Presto

Performance and Metadata Management

Metric Hudi Delta Lake Iceberg
Metadata Scalability Medium (timeline logs) Medium (JSON logs) High (tree-based metadata)
Write Performance High (MoR) High High
Read Performance High (CoW) Very High (with Z-Order) High
Compaction Required for MoR Optional None (append-only)

Iceberg stands out for large table performance and metadata handling, while Hudi shines in streaming ingestion and Delta excels in Spark-native workloads.

Integration Matrix

Engine Hudi Delta Lake Iceberg
Spark ✅ Native ✅ Native ✅ Native
Hive ✅ ⛔ Limited ✅
Flink ✅ ⛔ Experimental ✅
Presto/Trino ✅ ⛔ Partial ✅ Native
Athena ⛔ (via EMR) ⛔ ✅ (via Glue)

Iceberg offers the broadest engine compatibility, while Delta Lake is Spark-centric, and Hudi sits in the middle with strong batch and streaming capabilities.

When to Choose Which?

Choose Hudi when:

  • You need real-time ingestion and upserts
  • Building streaming CDC pipelines
  • Integrating with Hive or Spark

Choose Delta Lake when:

  • You run on Databricks or Spark-centric architectures
  • Need simple schema evolution and time travel
  • Focused on interactive BI or ML workloads

Choose Iceberg when:

  • You want an open and vendor-neutral architecture
  • Need partition evolution, scaling, and flexibility
  • Using Presto, Flink, or Trino in production

Conclusion

Choosing between Apache Hudi, Delta Lake, and Apache Iceberg depends on your data architecture, latency requirements, query patterns, and ecosystem.

  • Use Hudi for real-time ingestion and incremental pipelines.
  • Use Delta Lake for Spark-native analytics and versioned queries.
  • Use Iceberg for open architecture, scalability, and evolving schemas.

By aligning your use case with the right table format, you can future-proof your lakehouse architecture for both performance and flexibility.