Why Lakehouse Architecture Is Revolutionizing Data Analytics: Hudi vs Iceberg

Lakehouse Architecture Overview

When enterprises need an independent data warehouse to support BI and business analytics, they often adopt a hybrid "data lake + data warehouse" architecture, which raises construction, management, and development costs. With advances in big‑data technology, adding distributed transactions, metadata management, high‑performance SQL, and API capabilities to the data‑lake layer enables a unified lake‑warehouse architecture that supports both workloads.

Background and Related Projects

Traditional enterprise data lakes built on Hadoop or cloud storage provide semi‑structured and unstructured data for data science and machine learning, but lack strong consistency and SQL performance required by BI. Consequently, many organizations built separate data warehouses, leading to higher costs. Open‑source projects such as Apache Hudi (2017), Iceberg (2019), and Delta Lake (2020) aim to bring data‑warehouse capabilities to data lakes.

Key Features of Lakehouse Architecture

Support for multi‑model data (structured, semi‑structured JSON, unstructured).

Transactional guarantees ensuring consistency under concurrent operations.

Direct BI access on source data, reducing latency.

Unified data governance within the lake, minimizing data duplication.

Separation of storage and compute for independent scaling.

Open SQL and API interfaces enabling flexible machine‑learning integration.

Implementation Paths

Three main approaches are identified:

Extend Hadoop‑based data lakes with transactional and SQL capabilities to evolve into a lakehouse (e.g., Uber’s use of Hudi, StarRocks’ early work).

Build on cloud or third‑party object storage, adding Hadoop‑like layers or open‑source projects such as Iceberg for metadata and transaction support.

Leverage database‑native technologies that natively support multi‑model data and storage‑compute separation, exemplified by Snowflake and Databricks.

Apache Hudi

Hudi (Hadoop Upserts Deletes and Incrementals) was created by Uber to provide update, delete, and incremental processing on Hadoop. It offers two table formats: Copy‑on‑Write for fast reads with slower writes, and Merge‑on‑Read for fast writes with slower reads. Hudi uses MVCC with delta files, primary‑key‑based indexing, Bloom filters, and three read views (incremental, read‑optimized, real‑time) to support various analytics and machine‑learning scenarios.

Apache Iceberg

Netflix developed Iceberg to overcome Hive’s metadata bottlenecks and limited ACID support. Iceberg stores table metadata in immutable files, using manifest files to track data files and partitions, which eliminates costly filesystem calls for partition pruning. It provides serializable isolation via optimistic concurrency, supports position and equality deletes, and works on object storage without requiring POSIX file system features.

Iceberg’s design makes it suitable for large‑scale, high‑cardinality partitioned data common in online advertising and risk‑control workloads, though its transaction concurrency is weaker than Hudi’s.

Conclusion

This article introduced the lakehouse concept and compared Apache Hudi and Apache Iceberg, highlighting their architectures, strengths, and typical use cases. The next part will explore StarRocks’ Inceptor and Delta Lake technologies.