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As the world generates massive amounts of data every second, managing and processing this data efficiently has become a critical challenge for organizations.
This blog will deeply dive into three popular storage formats – Delta, Iceberg, and Hudi.
Delta is an open-source storage format that provides ACID (Atomicity, Consistency, Isolation, Durability) transactions and scalable metadata management for big data workloads. Delta combines Parquet for columnar storage and Apache Avro for data serialization, making it compatible with popular big data processing engines like Apache Spark, Apache Hive, and Apache Presto.
Delta offers several optimization techniques, including:
- Delta Log: Delta uses a transaction log, also known as the Delta Log, to store all changes made to the data. This allows for efficient updates, deletes, and appends on large datasets without requiring a full data rewrite.
- Time Travel: Delta supports time travel, allowing users to query data as it appeared at a specific time. This is useful for auditing, debugging, and data recovery purposes.
- Schema Evolution: Delta allows for schema evolution, allowing users to add, modify, or delete columns without requiring a rewrite of the entire dataset. This makes it flexible for handling changing data requirements.
- Delta Cache: Delta provides an in-built caching mechanism called Delta Cache, which allows users to cache a subset of data in memory for faster access.
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Iceberg is another open-source storage format that provides ACID transactions and schema evolution for big data workloads. Iceberg is designed to work with large datasets and supports popular big data processing engines like Apache Spark, Apache Hive, and Apache Presto.
Some of the optimization techniques offered by Iceberg include:
- Snapshot Isolation: Iceberg uses snapshot isolation to ensure that concurrent reads and writes do not interfere with each other, ensuring consistent data processing.
- Time Travel: Like Delta, Iceberg supports time travel, allowing users to query data at specific points in time.
- Metadata Management: Iceberg provides a metadata management system that stores schema information and statistics about the data, enabling efficient query optimization.
- Columnar Storage: Iceberg uses columnar storage to optimize query performance, allowing for efficient compression and encoding of data, reducing I/O overhead.
Hudi (Hadoop Upserts Deletes and Incrementals) is an open-source storage format for processing large-scale data on Apache Hadoop. Hudi supports incremental data updates, deletes, and upserts, making it suitable for use cases requiring frequent data changes.
Some of the optimization techniques offered by Hudi include:
- Write Optimizations: Hudi uses delta encoding and compression techniques to optimize data writes, reducing storage and I/O overhead.
- Indexing: Hudi supports indexing to speed up data retrieval and query performance.
- Schema Evolution: Hudi allows for schema evolution, similar to Delta and Iceberg, making it flexible for handling changing data requirements.
- Optimization: Delta, Iceberg, and Hudi all offer optimization techniques to improve data storage and processing performance. Delta and Iceberg support time travel, allowing users to query data at specific points in time and offer schema evolution, enabling flexibility in handling changing data requirements. Delta provides an in-built caching mechanism called Delta Cache, while Iceberg uses columnar storage to optimize query performance. Hudi focuses on write optimizations and indexing to improve data writes and query performance. All three formats offer various optimizations, but the specific techniques used may vary depending on the use case and requirements.
- Industrial Applications: Delta, Iceberg, and Hudi find applications in different industries based on their unique features. Delta is commonly used in finance, e-commerce, healthcare, and telecommunications industries, where real-time data processing, data lineage, and data versioning are critical requirements. Iceberg is often used in industries such as e-commerce, ad tech, gaming, and social media, where data consistency, scalability, and performance are crucial. Hudi is widely used in finance, healthcare, logistics, and advertising, where frequent data updates and real-time processing are common requirements.
- Compatibility: Delta and Iceberg are designed to work with popular big data processing engines such as Apache Spark, Apache Hive, and Apache Presto, providing seamless integration with existing big data ecosystems. Hudi, on the other hand, is specifically designed for Apache Hadoop. The compatibility of these formats may depend on an organization’s existing technology stack and infrastructure.
- Use Case Suitability: Delta, Iceberg, and Hudi are optimized for different use cases. Delta and Iceberg are well-suited for scenarios that require strong data consistency, data lineage, and versioning, making them suitable for use cases where data integrity is critical, such as financial applications or regulatory compliance. Hudi, on the other hand, is designed for scenarios that require frequent data updates and real-time processing, making it suitable for use cases such as real-time analytics, data streaming, and event processing.
Delta, Iceberg, and Hudi are three popular storage formats for big data workloads, each with unique features and optimizations. Delta provides ACID transactions, time travel, and schema evolution, making it suitable for real-time data processing and data lineage. Iceberg also offers ACID transactions, time travel, and metadata management, making it ideal for data consistency and scalability. Hudi is optimized for frequent data updates and real-time processing, making it suitable for use cases that require real-time analytics and event processing. The choice of storage format depends on the specific requirements of the use case, the existing technology stack, and the industrial application. Understanding these storage formats’ features, optimizations, and use case suitability can help organizations make informed decisions when dealing with big data workloads.
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Drop a query if you have any questions regarding Data Storage Format, I will get back to you quickly.
1. Can I use Delta or Iceberg with Apache Flink for stream processing?
ANS: – Currently, Delta and Iceberg are primarily designed to work with batch processing engines like Apache Spark, Apache Hive, and Apache Presto. However, ongoing efforts are to provide integrations with stream processing engines like Apache Flink. Delta has a feature called Delta Streaming, which enables the ingestion and processing of streaming data but is still in the experimental stage. Iceberg, on the other hand, does not have native support for stream processing at the moment. It’s always recommended to check the official documentation and updates from the respective projects for the latest information on stream processing support.
2. Can I use Hudi with Apache Spark for batch processing?
ANS: – Yes, Hudi is designed to work with Apache Hadoop and Apache Spark, and it provides native integration with these big data processing engines. Hudi provides APIs for reading and writing data using Spark DataFrame and RDD (Resilient Distributed Dataset) APIs, making it compatible with Spark batch processing workflows. You can leverage the Hudi library in your Spark applications to perform batch processing tasks like data ingestion, updates, and queries with real-time processing capabilities.
3. How does schema evolution work in Delta, Iceberg, and Hudi?
ANS: – Delta, Iceberg, and Hudi all support schema evolution, allowing you to evolve the schema of your data over time without disrupting existing data. In Delta, schema evolution is achieved through an “evolution” operation, which allows you to add, modify, or delete columns in the schema. Delta automatically handles schema evolution and ensures backward data compatibility during reads and writes. In Iceberg, schema evolution is achieved using metadata and schema snapshots, allowing you to evolve the schema while preserving data consistency. In Hudi, schema evolution is achieved through support for both explicit schema and schema-on-read approaches, allowing you to evolve the schema dynamically during reads and writes.
WRITTEN BY Sanjay Yadav
Sanjay Yadav is working as a Research Associate - Data and AIoT at CloudThat. He has completed Bachelor of Technology and is also a Microsoft Certified Azure Data Engineer and Data Scientist Associate. His area of interest lies in Data Science and ML/AI. Apart from professional work, his interests include learning new skills and listening to music.