Apache Iceberg vs Apache Hudi: Choosing the Right Data Management Solution for Your Big Data Needs

Here's a side-by-side comparison of Apache Iceberg and Apache Hudi:

Feature	Apache Iceberg	Apache Hudi
Data Storage	Table format with snapshot and transaction support, stored in cloud object stores (e.g., S3, ADLS) or HDFS.	Optimized for incremental data ingestion and updates, stored in cloud object stores (e.g., S3, ADLS) or HDFS.
Use Cases	- Batch and stream processing - ACID transactions - Schema evolution and versioning	- Incremental data processing - Change Data Capture (CDC) - Data Upserts and Deletes
Data Model	Structured tables with support for nested data and schema evolution.	Supports both row-based and columnar data formats with schema evolution and versioning.
Architecture	Designed for efficient reads and writes, with support for scalable metadata management and partition pruning.	Built for real-time and batch processing, with support for incremental data ingestion and efficient query performance.
Query Support	Supports SQL and provides integration with Apache Spark and Presto for analytics.	Provides support for SQL queries and integration with Apache Spark, Hive, and other Hadoop-based frameworks.
Performance	Optimized for high-performance reads and writes, with efficient metadata management and query optimization.	Designed for low-latency data ingestion and updates, with support for ACID transactions and compaction.
Ecosystem	Part of the Apache ecosystem with growing community adoption and support.	Widely used in the Hadoop and big data ecosystem, with contributions from major tech companies.
Maturity	Relatively newer project with ongoing development and improvements.	Established project with a mature codebase and active community support.
Use Case Focus	Primarily focused on table format and efficient data management for analytics workloads.	Geared towards real-time data processing and incremental data updates for operational use cases.

In summary, Apache Iceberg is focused on efficient data storage, schema evolution, and analytics workloads, while Apache Hudi specializes in incremental data processing, change data capture, and real-time updates for operational use cases. Both projects offer unique features and capabilities to address different requirements in the data processing and analytics domain.