In today’s data-driven world, businesses generate vast amounts of data daily. To manage and analyze this data efficiently, organizations are adopting a data lakehouse approach. This architecture combines the flexibility and scalability of data lakes with the performance and reliability of data warehouses.​

A data lakehouse is a modern data architecture that merges the scalability of data lakes with the structured querying and transaction support of data warehouses. It supports various data types, including structured, semi-structured, and unstructured data, making it an ideal solution for diverse analytics needs.​

However, managing data in lakehouses presents challenges. Traditional data lakes often store raw, unprocessed data, leading to issues in data consistency, schema enforcement, and efficient querying. This is where table formats come into play.​

A table format is a structured approach to organizing and managing data within a data lake. It introduces a metadata layer that defines how data files are organized, enabling features such as schema evolution, time travel, and ACID transactions. Table formats like Apache Iceberg, Delta Lake, and Apache Hudi have emerged to bring data warehouse-like capabilities to data lakes, ensuring consistency, reliability, and performance across various data processing engines.​

Among these, Apache Iceberg stands out as a high-performance, open-source table format designed to manage large-scale datasets in data lakes. Originally developed at Netflix and now an Apache Software Foundation project, Iceberg acts as a metadata layer that organizes how data is stored and accessed. It enables multiple engines like Spark, Trino, Flink, and Hive to safely work with the same tables concurrently, bringing the reliability and simplicity of SQL tables to big data.​

By implementing Apache Iceberg, organizations can build robust data lakehouses that offer scalable storage, efficient querying, and seamless integration with BI tools, all while maintaining data consistency and reliability.

Key Features of Apache Iceberg Tables

Platform Interoperability

Apache Iceberg is designed to be engine-agnostic, enabling multiple processing engines—such as Apache Spark, Flink, Hive, Trino, and Presto—to safely interact with the same table concurrently. This interoperability allows organizations to use diverse tools for data processing and analytics without the need for data duplication or migration. By maintaining data in open formats like Parquet, Iceberg ensures integration across various platforms, facilitating flexible and scalable data workflows.

ACID Transactions

Iceberg ensures data consistency and reliability through ACID (Atomicity, Consistency, Isolation, Durability) transactions. It achieves this by using snapshot isolation, allowing multiple writers to operate on the same table without conflicts. This design supports concurrent data operations and ensures that all changes are applied atomically, maintaining data integrity even in distributed environments.

Schema Evolution

Iceberg supports full schema evolution, enabling changes to the table schema without requiring a full rewrite of the data. Schema modifications such as adding, renaming, or reordering columns are managed through metadata updates, allowing for seamless adaptation to changing data requirements without disrupting existing data. ​

Time Travel & Snapshots

Iceberg maintains a complete history of table changes through snapshots, enabling time travel capabilities. Users can query historical versions of the data, compare changes over time, and roll back to previous states if necessary. This feature is particularly useful for auditing, debugging, and ensuring data consistency over time. ​

Efficient Query Performance

Iceberg optimizes query performance by leveraging its rich metadata layer. It stores detailed information about data files, including row counts, partition values, and column statistics, enabling query engines to perform metadata pruning. This allows for skipping irrelevant data files during query execution, significantly reducing I/O operations and improving query efficiency.

When to Use Iceberg Tables

Here are scenarios where adopting Iceberg tables is especially beneficial:​

Managing Large Datasets in Cloud Storage

When dealing with vast amounts of data stored in cloud object storage systems like Amazon S3, Google Cloud Storage, or Azure Blob Storage, Iceberg provides efficient data organization and querying capabilities. Its support for open file formats like Parquet allows for seamless integration and optimized performance without the need for data migration. ​

Enabling Multi-Engine Data Access

In environments where multiple teams use different data processing engines—such as Apache Spark, Flink, Trino or Hive—Iceberg’s engine-agnostic design allows these diverse systems to interact with the same dataset concurrently. This interoperability eliminates the need for redundant data copies and ensures consistency across various tools. ​

Implementing Incremental Data Processing

For use cases requiring frequent data updates, deletions, or insertions—such as handling Slowly Changing Dimensions (SCDs) or complying with data privacy regulations—Iceberg’s support for ACID transactions and schema evolution facilitates efficient incremental data processing. This capability ensures data integrity and simplifies data pipeline management.

Iceberg Integration with Snowflake

Snowflake integrates with Apache Iceberg by allowing users to query and manage Iceberg tables stored in external cloud storage. This integration makes use of two key components: external volumes and catalog integrations.

External Volumes

An external volume in Snowflake is a named object that connects Snowflake to an external cloud storage (such as Amazon S3, Google Cloud Storage, or Azure Storage). It stores the necessary identity and access management (IAM) credentials, enabling Snowflake to securely access data files, Iceberg metadata, and manifest files stored externally.

Catalog Integrations

Catalog integrations in Snowflake define how table metadata is organized and accessed. When using an external catalog like AWS Glue, a catalog integration allows Snowflake to interact with the external catalog to retrieve table metadata. This setup enables Snowflake to query Iceberg tables managed by external catalogs without the need to migrate data into Snowflake’s native storage.

By configuring external volumes and catalog integrations, Snowflake users can query and manage Iceberg tables stored in external cloud storage, benefiting from the scalability and performance of Snowflake’s analytics engine while maintaining data in open formats. Iceberg table managed by snowflake catalog have ability to perform CRUD operations on the table.

Advantages of using Iceberg Tables

Utilizing Apache Iceberg tables within Snowflake offers a powerful combination of flexibility, performance, and cost efficiency. By storing data externally in open formats like Parquet, organizations can maintain interoperability across various platforms like AWS Glue and spark, eliminating the need for data duplication or migration. Additionally, leveraging external cloud storage allows for scalable and cost-effective data management, while still benefiting from Snowflake’s robust analytics capabilities. Also, data from Iceberg tables can be joined with internal Snowflake tables, enabling comprehensive analytics without the need to migrate data into Snowflake.

In contrast to other open table formats like Delta Lake and Apache Hudi, Apache Iceberg stands out for its broad compatibility and flexibility. Iceberg is fully open-source and designed to work seamlessly with multiple processing engines such as Apache Spark, Flink and AWS Glue. This eliminates the need to rely on a single processing framework, unlike Delta Lake, which is more tightly integrated with the Databricks ecosystem. Additionally, Iceberg supports advanced features like partition evolution, allowing changes to partitioning strategies without requiring a full rewrite of the underlying data. These capabilities make Iceberg a powerful choice for building flexible and efficient data lakehouse solutions.

Limitations of using Iceberg Tables

Apache Iceberg also has certain limitations, particularly when used with platforms like Snowflake. One major challenge is catalog synchronization. When multiple engines access or modify the same table, keeping their catalogs in sync can be difficult. This can lead to inconsistent or outdated metadata reads. Another limitation involves read-only access to external Iceberg tables in Snowflake. Although Snowflake can read Iceberg tables stored in external cloud storage through an external volume and catalog, these tables are typically read-only unless they are managed by the Snowflake catalog, preventing write operations such as inserts, updates, or deletes. Lastly, while Iceberg is engine-agnostic by design, compatibility issues can arise between different engines and catalogs. Tables created using specific catalogs like AWS Glue or Snowflake’s internal catalog may not work seamlessly across all engines. Moreover, support for features like time travel and schema evolution may vary, with some engines lacking full or any support for these capabilities.

In conclusion, Apache Iceberg emerges as a robust and versatile table format, effectively bridging the gap between the flexibility of data lakes and the reliability of data warehouses. By introducing a sophisticated metadata layer, Iceberg empowers organizations to manage large-scale datasets with enhanced consistency, dependability, and performance. Its core features, including platform interoperability, ACID transactions, schema evolution, time travel, and efficient query optimization, directly address common challenges in data lake environments. Ultimately, by implementing Apache Iceberg, businesses can construct powerful data lakehouses that offer scalable storage, streamlined querying, and seamless integration with business intelligence tools, all while upholding data integrity and trustworthiness.