Mastering Open Table Formats: A Guide to Apache Iceberg, Hudi, and Delta Lake

Unlocking the Power of Open Table Formats: Apache Iceberg, Hudi, and Delta Lake for Scalable, Reliable Data Management

In the era of big data, managing large datasets efficiently has become a key concern for organizations across industries. Data engineers and analysts deal with massive amounts of information every day, and to derive value from it, they rely on well-structured storage formats that enable seamless data management, processing, and analytics. Traditional data lake architectures have provided a foundation for storing raw data, but they come with challenges like lack of consistency, difficulty in version control, and performance bottlenecks.

Enter Open Table Formats — a set of technologies designed to solve many of these issues by providing structure, governance, and performance improvements on top of traditional data lakes. In this article, we’ll explore the basics of open table formats and dive into three of the most popular frameworks: Apache Iceberg, Apache Hudi, and Delta Lake.

What Are Open Table Formats?

Open table formats are systems that manage large datasets on distributed storage by providing an abstraction layer that enables transactions, schema evolution, and efficient data management. They sit on top of data stored in cloud object storage like Amazon S3, Google Cloud Storage, or Hadoop Distributed File System (HDFS). Instead of just storing files and directories, these formats introduce concepts like tables, schemas, and partitions, making data easier to query and manage.

They are called “open” because they are built on open-source principles, making them community-driven and allowing for interoperability across different tools and platforms.

The three major open table formats we will cover are:

1. Apache Iceberg (Apache Foundation)
2. Apache Hudi (Apache Foundation)
3. Delta Lake (Linux Foundation)

Why Open Table Formats?

While traditional data lakes were ideal for storing unstructured data, they often suffer from limitations such as:

• Lack of ACID Transactions: Traditional data lakes don’t support transactions, leading to data corruption and inconsistent reads when multiple jobs access the same data.
• Schema Management: As data evolves, schemas change. Data lakes do not natively support schema evolution, making it hard to accommodate changes without extensive reprocessing.
• Performance: Querying data stored in raw formats such as CSV, Parquet, or ORC often leads to suboptimal performance, especially when data is spread across different partitions.

Open table formats were introduced to mitigate these issues, offering:

• ACID compliance: Ensures reliable, transactional data updates.
• Schema evolution: Allows dynamic changes to the schema without breaking downstream processes.
• Time Travel: Ability to query historical versions of data.
• Efficient data management: Open table formats are designed to handle massive datasets while ensuring efficient reads and writes.

1. Apache Iceberg

Apache Iceberg was created at Netflix to solve the challenges of managing huge datasets in cloud object stores. It is designed to provide a high-performance, scalable table format with support for ACID transactions and schema evolution.

Key Features:

• ACID Transactions: Ensures data integrity with atomic operations.
• Partition Evolution: Supports changing partition strategies without rewriting entire datasets.
• Schema Evolution: Allows for adding, deleting, or updating columns without needing to rewrite the data.
• Hidden Partitioning: Automatically optimizes partitioning for performance without burdening users with manual partition definitions.
• Time Travel: Lets users access previous versions of the table for debugging or audits.

Iceberg uses a manifest-based approach where each snapshot of the table points to manifests that describe the data files. This allows Iceberg to manage changes without requiring constant rewrites.

Resources for further reading:

Here are the resources to learn more about Iceberg:

• Official Documentation
• Integration with Apache Spark
• Read Delta Tables from Iceberg Clients (for Databricks)
• Integration with Snowflake

Real World Use Cases:

• Large-Scale Data Lakes: Ideal for organizations handling massive datasets, such as media streaming companies like Netflix. Apache Iceberg’s scalability and partition evolution features make it suitable for managing and querying large volumes of structured and semi-structured data. According to the Netflix Technology Blog, Iceberg was developed to handle the unique demands of their data warehouse, supporting scalability and optimized data access for large-scale analytics.
• High-Reliability Scenarios: Suitable for applications requiring strict data integrity and consistency with ACID transaction support. Iceberg’s transactional capabilities ensure reliable updates and consistent reads, making it ideal for industries with regulatory and data compliance requirements, like finance or healthcare. “Iceberg’s support for ACID transactions and schema evolution enables companies to ensure data consistency and integrity in complex, high-stakes environments,” as highlighted in the Apache Iceberg documentation.
• Scalable, Long-Term Storage with Governance: Best for environments that require both scalability and robust data governance, such as e-commerce or retail. Iceberg’s schema and partition evolution support allow organizations to adapt to changing data requirements without costly rewrites, ensuring efficient, compliant data management over time. The AWS Big Data Blog notes that Iceberg’s design makes it compatible with S3 and other cloud storage, providing flexibility for companies managing data growth and governance across hybrid cloud environments.

2. Apache Hudi

Apache Hudi (Hadoop Upsert Delete and Incremental) originated at Uber to support fast-changing data with upserts and incremental data streams. It focuses heavily on use cases involving low-latency updates and transactional data, making it an excellent choice for environments where data is constantly changing, such as in streaming or real-time analytics.

Key Features:

• Upserts and Deletes: Supports updating and deleting records efficiently without needing to rewrite entire partitions.
• Incremental Queries: Provides support for querying only the data that has changed since the last query (incremental queries), reducing the amount of data that needs to be read.
• ACID Transactions: Like Iceberg, Hudi supports ACID transactions to guarantee data consistency.
• Indexing: Hudi builds indexes to speed up the process of finding records for upserts or deletes.

Different Hudi Table Types:

• Copy-on-Write (COW): Default mode, where updates are written to new Parquet files.
• Merge-on-Read (MOR): Data is written to log files first, and merged lazily when read.

Resources for further reading:

Here are the resources to learn more about Hudi:

• Hudi Design and Concepts (also known as Hudi Stack)
• Apache Spark Quick Start or Apache Spark Integration with Hudi
• Apache Flink Quick Start or Apache Flink Integration with Hudi

Real World Use Cases:

• Real-Time Data Ingestion and Processing: Apache Hudi is designed for scenarios where data is constantly changing and needs to be updated in near-real time. “Hudi provides a unique approach to managing and ingesting real-time data, especially suited for high-velocity data pipelines,” states the Uber Engineering blog, highlighting Hudi’s upsert and delete capabilities.
• Incremental Data Pipelines: With Hudi’s ability to track and process only the changes in data (incremental queries), it’s an optimal choice for incremental ETL (Extract, Transform, Load) workflows. As the Apache Hudi project documentation explains, “incremental queries enable more efficient processing by only focusing on updated records, significantly reducing read volumes in data lake operations” (Apache Hudi Documentation).
• Frequent Upserts and Deletes: For industries like logistics and finance, where records need constant updating, Hudi’s upsert functionality offers a clear advantage. “The ability to upsert records without rewriting entire partitions makes Hudi a powerful choice for environments needing frequent updates,” as noted in Uber’s post onbuilding a large-scale transactional data lake, which discusses Hudi’s efficiency in managing mutable data in data lakes.

3. Delta Lake

Developed by Databricks, Delta Lake is an open-source project that brings reliability to data lakes by adding ACID transaction support and schema management on top of Apache Spark and cloud storage solutions. Delta Lake’s focus is on ensuring reliable, scalable data pipelines with a strong emphasis on high performance.

Key Features:

• ACID Transactions: Delta Lake supports ACID transactions, ensuring that batch and streaming data is written reliably. Here is the article on ACID transactions using Delta Lake.

Delta Lake Essentials: CRUD Operations and Data Management with SQL

• Schema Enforcement and Evolution: Delta enforces the schema at write time, ensuring that only data adhering to the schema is written. It also allows for schema evolution, making it easier to add or change fields over time.
• Time Travel: Similar to Iceberg, Delta Lake supports querying previous versions of the data, enabling easy auditing and debugging.
• Unified Batch and Streaming: Delta Lake allows you to handle batch and streaming data in the same pipeline, unifying the two types of processing.
• Z-Ordering: For improving query performance, Delta Lake supports data layout optimization using Z-ordering, which allows efficient access to frequently queried columns.

Real World Use Cases:

• High-Performance Data Lakes for Analytics: Delta Lake’s optimization features, such as Z-ordering and data layout optimization, significantly enhance query performance, making it ideal for analytics-driven applications. For instance, Adobe Experience Platform utilizes Delta Lake to manage petabytes of data, enabling efficient data processing and analytics. Delta
• Scalable Data Pipelines: Delta Lake supports both batch and streaming data in a unified pipeline, allowing organizations to process large datasets seamlessly. T-Mobile’s Data Science and Analytics Team migrated to a Delta Lake-based architecture to handle increased data volumes and complexity, resulting in improved scalability and performance. Delta
• Unified Batch and Streaming Data Processing: Delta Lake enables organizations to manage both batch and streaming workloads within the same system, providing a cohesive data processing approach. This capability is essential for industries like finance and retail, where real-time insights and up-to-date data are crucial for decision-making and operational efficiency. Databricks

Resources for further reading:

Here are the resources to learn more about Delta:

• Getting Started with Delta Lake Format (default with Apache Spark)
• Additional Tutorials
• CRUD Operations using Delta Lake in Databricks

Comparison: Iceberg vs. Hudi vs. Delta Lake

Iceberg vs. Hudi vs. Delta Lake

Conclusion

Open table formats like Apache Iceberg, Apache Hudi, and Delta Lake represent the future of data management in the cloud. By providing the tools necessary for managing large datasets with ACID transactions, schema evolution, and time travel, they bring structure and performance improvements to data lakes. Each system is designed with specific use cases in mind, and the choice of format will depend on your organization’s specific needs.

If you’re looking for scalability with rich governance features, Iceberg may be the ideal choice. If real-time data processing with upserts and incremental queries is your focus, Hudi is built for that. And if you’re building a high-performance pipeline for analytics with Spark, Delta Lake is a solid, reliable option.

Choosing the right open table format can drastically simplify your data architecture, reduce technical debt, and provide the performance and scalability your organization needs to thrive in the age of big data.