Comparing Apache Hudi, Apache Iceberg, and Delta Lake

Overview

Modern data management requires powerful data lake frameworks that efficiently handle large-scale data. The most popular formats today are Apache Hudi, Apache Iceberg, and Delta Lake. These technologies enhance data lakes by providing ACID (Atomicity, Consistency, Isolation, Durability) guarantees, version control, and data optimization capabilities, making data lakes more reliable and scalable. This blog will explore these three technologies, compare their features, and help you understand which might suit your needs.

Introduction

1. Apache Hudi: Developed by Uber, Apache Hudi (Hadoop Upserts Deletes and Incrementals) provides data lake users with the capability to handle streaming and batch processing on the same data. Hudi enables efficient data ingestion with upsert capabilities, allowing users to update, insert, and delete data in a lake storage environment. It provides near real-time data freshness with reduced latency and is particularly suited for use cases requiring fast data updates.
2. Apache Iceberg: Apache Iceberg, created by Netflix, focuses on high-performance, large-scale analytics on data lakes. It offers a table format for huge analytics datasets, allowing users to manage petabyte-scale data with reliability and speed. Iceberg supports schema evolution, hidden partitioning, and time travel queries, making it ideal for analytical use cases where schema changes and querying older data versions are common.
3. Delta Lake: Developed by Databricks, Delta Lake is an open-source storage layer that brings ACID transactions to data lakes. It enhances data lakes with features such as data versioning, scalable metadata handling, and data quality through schema enforcement. Delta Lake is tightly integrated with Apache Spark, making it an excellent choice for Spark-based workloads requiring reliable data processing.

Comparisons

Advantages

1. Apache Hudi:

• Version Control: Supports data versioning, enabling time travel queries and rollback capabilities, which helps track changes over time.
• Indexing Mechanism: Hudi’s built-in indexing speeds up read and write operations, enhancing overall query performance.
• Integration Flexibility: Works well with Spark, Flink, and Hive, allowing users to choose their preferred data processing engines without vendor lock-in.
• Data De-duplication: Prevents data duplication during ingestion, ensuring clean, accurate data in data lakes.
• Compaction Support: Allows compaction of small files into larger ones, optimizing storage and improving read efficiency.

2. Apache Iceberg:

• Partition Evolution: Allows partitions to evolve without manual intervention, simplifying managing large datasets and reducing maintenance overhead.
• Enhanced Security: Provides row-level filtering and column masking, which helps enforce security and privacy policies on sensitive data.
• Metadata Management: Advanced metadata management helps track data changes, making data querying faster and more efficient.
• Rollback and Snapshot Isolation: Enables users to easily revert to previous data states, ensuring data consistency during large-scale processing.
• Engine Interoperability: Supports a wide range of data processing engines such as Spark, Flink, Presto, and Trino, enhancing its adaptability in various ecosystems.

3. Delta Lake:

• Efficient File Management: Optimizes storage by compacting small files into larger ones, reducing overhead and enhancing query performance.
• Schema Enforcement and Evolution: Enforces schema at runtime, which helps maintain data quality and allows schemas to evolve as data requirements change.
• Built-In Data Quality Constraints: Ensures data integrity with constraints such as not-null, unique, and primary key checks, making it suitable for critical applications.
• Delta Sharing: Enables secure data sharing across different platforms, maintaining data privacy and integrity.
• Streaming Capabilities: Supports continuous data streaming into tables, seamlessly blending batch and streaming data processing for real-time analytics.

Conclusion

Choosing the right data lake format depends on your specific needs.

Each technology has unique strengths and understanding your workload requirements will help guide your choice.

Drop a query if you have any questions regarding Apache Hudi, Apache Iceberg, or Delta Lake and we will get back to you quickly.

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