by Jerod Johnson | January 10, 2025

Apache Iceberg vs. Delta Lake: 7 Crucial Differences & Which Should You Choose?

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Data lakes have evolved rapidly to accommodate the demands of modern data management, resulting in the rise of innovative table formats like Apache Iceberg and Delta Lake. These formats are designed to optimize storage, enable efficient data access, and support data governance, offering a structured layer within data lakes for easier querying and management, which, in turn, helps fuel increasingly popular artificial intelligence and machine learning (AI/ML) initiatives. Choosing the right table format is critical for organizations looking to maximize data lake performance and scalability, and both Iceberg and Delta Lake have unique strengths and use cases.

In this article, we’ll examine what sets Apache Iceberg and Delta Lake apart, analyzing their architecture, key features, advantages, and limitations. By the end, you’ll have a clearer understanding of which table format may be best suited to your data infrastructure needs.

What is Apache Iceberg?

Apache Iceberg, originally developed by Netflix and now an Apache project, emerged to solve the common challenges associated with big data storage, particularly around managing large tables and complex data structures. Iceberg provides an open table format built for scalability, enabling users to work efficiently with petabytes of data.

Key features of Apache Iceberg

  • Data versioning and schema evolution: Iceberg supports flexible schema evolution, allowing for changes without disrupting existing data. This feature is crucial for organizations managing rapidly evolving datasets.
  • Partitioning and performance improvements: Iceberg optimizes data access through hidden partitioning and automatic file management, resulting in faster queries and improved efficiency for large datasets.
  • Atomic operations: Iceberg enables atomic transactions, which ensure data consistency during concurrent writes or updates, supporting multiple users and minimizing data conflicts.

Popular use cases for Apache Iceberg

Organizations in industries such as media, e-commerce, and finance have adopted Iceberg for its robust data management capabilities, using it for high-volume transaction processing, batch processing, and real-time analytics.

What is Delta Lake?

Delta Lake, an open-source project initiated by Databricks, is designed to add reliability, scalability, and performance to data lakes through its support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. Built with a deep integration with the Apache Spark ecosystem, Delta Lake has become a popular choice for users looking to bring data lake capabilities into their machine learning and real-time data pipelines.

Key features of Delta Lake

  • ACID transactions: Delta Lake’s ACID (atomicity, consistency, isolation, and durability) compliance allows for consistent data handling, ensuring data integrity during complex operations, including concurrent reads and writes.
  • Efficient data compaction and indexing: Delta Lake optimizes data storage by compacting small files and indexing frequently accessed data, improving query performance.
  • Time travel: Delta Lake supports data versioning with time travel, allowing users to query and restore previous versions of data, which is invaluable for auditing, troubleshooting, and data recovery.

Common use cases for Delta Lake

Delta Lake is widely used in sectors like finance, telecommunications, and retail, particularly for real-time data processing, ML model training, and data warehousing applications.

7 key differences between Apache Iceberg and Delta Lake

Understanding the nuances of Iceberg and Delta Lake is essential to choosing the right fit. Here are the primary differences:

Metadata handling

Apache Iceberg: Iceberg uses a flexible metadata layer that scales with table size, storing metadata as Apache Parquet files. This structure is particularly effective for handling large volumes of data while maintaining fast query performance.

Delta Lake: Delta Lake uses a transaction log for metadata management, recording every change to the table. This log-based approach is well-suited to real-time analytics but can grow large, requiring periodic clean-up for optimal performance.

For more information on other options for metadata handling, check out our blog on top data catalog tools.

Toolset integration

Apache Iceberg: Integrates natively with Apache Spark, Trino, Presto, and other engines, making it a versatile choice for users working with multiple tools.

Delta Lake: Offers seamless integration with Apache Spark and is tightly coupled with the Databricks ecosystem, which can be a strong advantage for users in Spark-centric environments.

Read/write features

Apache Iceberg: Offers read-optimized file layouts and supports append-only and read-modify-write operations. This versatility makes it suitable for both analytical and transactional workloads.

Delta Lake: Primarily optimized for write-heavy workloads and ACID compliance, making it an ideal choice for users needing robust consistency during high-frequency data changes.

Data versioning and time travel

Apache Iceberg: Supports data versioning with both snapshot and rollback capabilities. This functionality enables users to track changes and revert data, useful for historical analysis.

Delta Lake: Offers time travel through a built-in version history, allowing users to query previous data states by timestamp or version number, which is valuable for debugging and audits.

Table services

Apache Iceberg: Provides services like compaction and garbage collection to help manage and optimize table storage automatically.

Delta Lake: Features built-in file compaction to manage small files, enhancing performance for large datasets.

Ecosystem support

Apache Iceberg: Due to its open-source origins and wide industry adoption, Iceberg enjoys support from multiple vendors, making it compatible with diverse infrastructures.

Delta Lake: Supported natively within Databricks, Delta Lake integrates well within this environment but may require more setup for users outside the Databricks platform.

Platform support

Apache Iceberg: Supports cloud-native storage formats such as AWS S3, Azure Data Lake Storage, and Google Cloud Storage, providing flexibility for users across cloud environments.

Delta Lake: Works seamlessly with AWS and Azure within the Databricks platform, with some limited support for external storage layers.

How to choose the right format for your business

When deciding between Apache Iceberg and Delta Lake, consider the following factors:

  • Data consistency requirements: For businesses that prioritize ACID compliance and high-frequency data updates, Delta Lake’s transaction log and ACID support are compelling.
  • Infrastructure and tool compatibility: If your infrastructure relies on multi-engine compatibility (e.g., Apache Spark, Trino), Apache Iceberg’s open-source compatibility offers greater flexibility.
  • Workload type: Delta Lake is optimized for real-time analytics and streaming, making it ideal for time-sensitive applications. Iceberg, on the other hand, excels in high-volume batch processing and read-heavy workloads.
  • Long-term data retention: For organizations focused on historical data access, both formats offer versioning, but Iceberg’s Parquet-based metadata may provide easier scaling for long-term storage.

Uplevel your connectivity to any data lake with CData Virtuality

CData Virtuality empowers businesses to seamlessly integrate with data lakes using formats such as Apache Iceberg and Delta Lake. Virtuality simplifies connectivity across diverse data sources, enabling real-time data access and insights. Whether your infrastructure relies on Iceberg, Delta Lake, or both, CData Virtuality provides a streamlined approach to data virtualization, helping you unlock the full potential of your data lake.

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