Comparative Analysis of Apache Hudi, Delta Lake, and Apache Iceberg for Lakehouse Architectures

Introduction

As Lakehouse architectures become increasingly popular, interest in the open‑source projects that form their core—Apache Hudi, Delta Lake, and Apache Iceberg—has grown. Most existing comparisons evaluate these projects only as traditional append‑only table/file formats, overlooking qualities essential for modern data‑lake platforms that must support heavy update workloads through continuous table management. This article dives deeper into Apache Hudi’s technical distinctions and explains why it is a mature data‑lake platform that leads the others.

Feature Comparison

Below is an overall feature comparison. Notice how the Hudi community invests heavily in building comprehensive platform services on top of lake storage formats. While format standardization and interoperability are crucial, the table/platform services provide a powerful toolkit for developing and managing data‑lake deployments.

Feature Highlights

Building a data‑lake platform is more than checking off functional boxes. The following differentiated features are explored in simple English with use‑case examples and real benefits.

Incremental Pipelines

Most data engineers feel forced to choose between streaming and traditional batch ETL pipelines. Apache Hudi introduces a new paradigm called incremental pipelines. Out‑of‑the‑box, Hudi tracks all changes (inserts, updates, deletes) and exposes them as a change stream. Record‑level indexing enables efficient use of this stream to avoid recomputation and process changes incrementally. While other lake platforms may offer incremental consumption, Hudi is designed for low‑latency, cost‑effective ETL pipelines.

Databricks recently added a similar feature called Change Data Feed, which remained proprietary until open‑sourced in Delta Lake 2.0. Iceberg supports incremental reads but only for appends, lacking update/delete support needed for true CDC and transactional data.

Concurrency Control

ACID transactions and concurrency control are key Lakehouse features, yet real‑world workloads expose challenges. Hudi, Delta, and Iceberg all support optimistic concurrency control (OCC). In OCC, writers check for overlapping files and retry on conflict. Delta Lake’s OCC is a JVM‑level lock on a single Spark driver node, limiting multi‑cluster scenarios. Hudi’s concurrency control is finer‑grained at the file level and optimized for many small updates/deletes, dramatically reducing conflict likelihood in realistic workloads.

Merge on Read

All three projects write data to Parquet files and version them on updates (copy‑on‑write). Hudi also supports Merge‑On‑Read (MoR), combining columnar Parquet files with row‑based Avro log files. Updates are batched in log files and later compacted to Parquet, balancing query performance with write amplification. This enables near‑real‑time streaming workloads to use efficient row‑oriented formats while batch workloads benefit from vectorized columnar reads.

Thus, Hudi can use a row‑oriented format for low‑latency streams and a columnar format for batch, seamlessly merging the two when needed.

Partition Evolution

Iceberg emphasizes hidden partitions to enable partition evolution—updating partition schemes without rewriting existing data. Hudi takes a different approach: it allows coarse‑grained or no partitioning, then applies fine‑grained clustering within each partition, which can evolve asynchronously without rewriting data, comparable to Snowflake’s micro‑partitioning.

Multi‑Mode Indexing

Indexes are essential in databases but largely absent in data lakes. Recent Hudi releases introduce a first‑of‑its‑kind high‑performance indexing subsystem called Hudi Multi‑Mode Index. It provides asynchronous indexing without affecting write latency, supporting Bloom, Hash, Bitmap, R‑tree, etc. Index files are stored in Hudi metadata tables alongside data, offering 10‑100× faster point lookups and 10‑30× overall query performance improvements in real‑world workloads.

Ingestion Tools

Hudi’s standout ingestion utility is DeltaStreamer, a battle‑tested, production‑grade tool that incrementally ingests changes from sources such as DFS, Kafka, CDC logs, S3 events, JDBC, etc. Iceberg lacks a managed ingestion utility, and Delta’s Autoloader remains a proprietary Databricks feature.

User Cases: Community Examples

Feature comparisons and benchmarks help newcomers, but evaluating personal use cases is essential. All three technologies have origins and strengths: Iceberg (Netflix) solves cloud‑scale file‑list issues; Delta (Databricks) integrates deeply with Databricks Spark; Hudi (Uber) supports near‑real‑time PB‑scale lakes with painless table management.

When workloads go beyond simple append‑only operations, Hudi often holds a technical advantage. Once you handle many updates, need higher concurrency, or aim to reduce end‑to‑end pipeline latency, Hudi leads in performance and feature set.

Amazon Package Delivery System

ATS processes PB‑scale data with continuous inserts, updates, and deletes. Using AWS Glue Spark jobs and DeltaStreamer, they achieve real‑time ingestion at hundreds of GB per hour on a PB‑scale lake.

ByteDance / Douyin

Handles 400 PB+ tables with daily PB‑scale increments, requiring >100 GB/s throughput and complex, high‑dimensional schemas. Hudi was chosen for its openness, global indexing, and customizable storage logic.

Walmart

Robinhood

Needed low data freshness latency; shifted from daily batch to hourly or faster pipelines, using Hudi’s incremental ingestion from Kafka and its record‑level index for efficient upserts.

Zendesk

Captures CDC from 1,800+ Aurora MySQL databases via AWS DMS, processes changes with Amazon EMR and Hudi, storing PB‑scale event data in S3 as Hudi tables for Athena queries.

GE Aviation

Integrated Hudi into CDC pipelines, reducing code overhead and focusing on system reliability; now managing over 10,000 tables and 150+ source systems.

Given the rapid evolution of Lakehouse technology, it’s important to recognize where open‑source innovation originates. Many core Lakehouse features now trace back to ideas first introduced by Hudi.

When selecting a Lakehouse technology, evaluate your own use case. Feature comparison sheets and benchmarks should not be the sole decision factor; this article aims to provide a starting point and reference. Apache Hudi is innovative, battle‑tested, and here to stay. Join the Hudi Slack to ask questions and collaborate with a vibrant global community.

If you would like a one‑on‑one consultation to dive deeper into your use case and architecture, feel free to contact [email protected]. We have decades of experience designing, building, and operating some of the world’s largest distributed data systems.