How Delta Lake, Apache Iceberg, and Hudi Optimise Data Lakehouse Architectures

By Olakunle Ebenezer Aribisala

Data lakehouses have become a popular architectural pattern, merging the flexibility of data lakes with the governance and reliability of traditional data warehouses. Three major technologies, such as Delta Lake, Apache Iceberg, and Apache Hudi, have emerged as leading options. Each offers transactional capabilities, efficient query performance, and strong data governance features. Below is a comparative analysis presented in this listicle.

1. Delta Lake Balances Batch and Streams Workloads
   Delta Lake, originally developed by Databricks, enhances cloud data lakes by adding transactional support and schema enforcement. It ensures data integrity through atomic operations, enables historical dataset queries for better auditability, and supports schema evolution to maintain data quality while allowing changes. On the performance side, Delta Lake improves query execution using Z-order indexing for efficient data scans and delivers high throughput for both batch and streaming writes.
2. Apache Iceberg Offers Large-Scale Analytical Flexibility
   Apache Iceberg was first created by Netflix and is now an Apache project that prioritises openness, scalability, and reliability. Its open specification ensures broad interoperability across different platforms, while hidden partitioning abstracts physical partitioning to optimise query planning automatically. Iceberg also supports snapshot-based version control and rollback, enabling efficient data management. Performance-wise, it reduces I/O operations through incremental data scans and allows rapid schema evolution for changing analytical requirements.
3. Apache Hudi, Designed for Incremental and Streaming Data
   Apache Hudi, developed by Uber, focuses on incremental data processing and change data capture (CDC) workflows. It handles incremental updates efficiently, manages mutable data through upsert and delete capabilities, and integrates real-time streaming ingestion. Hudi’s performance is particularly strong for frequent incremental updates, with compaction mechanisms that minimise latency and improve real-time analytics.
   Comparative Performance Analysis
   Transaction Management Compared: Delta Lake offers robust transaction handling with optimised metadata processing, while Iceberg proves highly efficient for large datasets and complex transactions. Hudi, on the other hand, performs best for frequent incremental transactions but is slightly less suited to massive batch transactions.

Query Efficiency Across the Technologies: Delta Lake achieves strong query performance through data skipping and Z-order clustering. Iceberg excels with metadata-based pruning for complex analytical workloads. Hudi delivers solid results for incremental and streaming analytics, though it falls slightly behind when handling large-scale batch queries.

Ease of Implementation and Ecosystem Integration: Delta Lake integrates smoothly with the Databricks ecosystem but requires moderate configuration elsewhere. Iceberg, being vendor-neutral, offers flexibility but demands more technical expertise for optimal setup. Hudi integrates well into existing ecosystems, especially where streaming and incremental workloads dominate.

Making the Right Choice: The decision between Delta Lake, Apache Iceberg, and Apache Hudi depends on specific workload requirements, scalability goals, and the existing technology stack. Delta Lake provides balanced support for both batch and streaming workflows, Iceberg leads for large-scale analytical tasks, and Hudi stands out in streaming and incremental processing scenarios.