Unlocking the Power of Apache Iceberg on AWS: Features, Migration Approaches, and Best Practices

Apache Iceberg is a powerful open-source table format that simplifies data lake management while delivering high performance, scalability, and reliability. It brings the simplicity and consistency of traditional SQL tables to large-scale data environments.

In modern AWS Lakehouse architectures, Apache Iceberg serves as a foundational technology by combining the flexibility and cost-efficiency of Amazon S3-based data lakes with the advanced capabilities of data warehouses. AWS services such as Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, and Amazon SageMaker Lakehouse provide native support for Iceberg, enabling seamless data interoperability across the analytics ecosystem.

With advanced capabilities such as ACID transactions, schema evolution, time travel, hidden partitioning, and optimized metadata management, Apache Iceberg helps organizations ensure data consistency, simplify operational complexity, improve governance, and accelerate query performance for large-scale analytics, machine learning, and AI workloads.

Key Features of Apache Iceberg

Apache Iceberg offers a powerful collection of features that simplify data lake management. These capabilities help organizations build scalable, governed, and future-ready analytics platforms:

1. ACID Compliance
   Apache Iceberg extends full ACID (Atomicity, Consistency, Isolation, and Durability) transaction capabilities to data lakes, enabling reliable, consistent, and concurrent data operations on object storage platforms such as Amazon S3.

2. Scalable metadata architecture
   It enables organizations to efficiently manage petabyte-scale datasets comprising billions of files without encountering traditional metadata scalability challenges. By maintaining a structured and hierarchical metadata architecture, Apache Iceberg accelerates table planning, optimizes query execution, and ensures fast, reliable access to data even as datasets continue to grow and complexity.

3. Schema enforcement and evolution
   It delivers robust schema enforcement and schema evolution capabilities, allowing organizations to maintain data integrity and consistency while seamlessly adapting to evolving business requirements. These capabilities make Iceberg particularly well-suited for enterprise-scale data lake and Lakehouse implementations. Iceberg supports a wide range of schema modifications, including adding, dropping, renaming, updating, and reordering columns, without requiring costly table rewrites or disrupting existing workloads.

4. Time Travel
   It preserves historical snapshots of table data, enabling users to query and analyse datasets as they existed at a particular point in time. This powerful time-travel capability supports auditing, troubleshooting, data validation, and historical analysis, allowing organizations to investigate changes, recover from errors, and gain insights from previous versions of their data with confidence.

5. Hidden Partitioning
   Apache Iceberg simplifies partition management through its hidden partitioning capability, automatically performing partition pruning behind the scenes. This eliminates the need for users to understand complex underlying partition structures or explicitly include partition filters in queries, resulting in simpler query development, improved usability, and optimized query performance.

6. Open File Format Support
   Iceberg is compatible with several industry-standard open-source file formats, including:

  - Apache Parquet
  - Apache Avro
  - Apache ORC

This flexibility enables interoperability across a wide range of analytics engines and data processing frameworks.

Migrating existing tables to Iceberg

There are two primary approaches for migrating existing Hive-style tables to Apache Iceberg, depending on the organization's migration objectives, data volume, and operational requirements:

In-place migration

• It is a metadata-only conversion approach that enables organizations to adopt Apache Iceberg without rewriting existing data files.

• Instead of moving or transforming the underlying data, Iceberg generates and maintains metadata files that reference the current datasets. This approach significantly reduces migration time, minimizes operational overhead, and lowers costs.

• By preserving the existing data files while adding an Iceberg metadata layer, organizations can quickly leverage Iceberg's advanced capabilities with minimal disruption to ongoing workloads.

• There are two main options available to implement in-place migration:
  a. Snapshot
  The snapshot migration approach creates a new Iceberg table with a different name while preserving the schema and partitioning structure of the source table. The original source table remains completely unchanged throughout the process, ensuring zero impact on existing workloads. By enabling users to evaluate Iceberg capabilities without modifying the original dataset, snapshot migration provides a low-risk pathway for assessing and adopting Apache Iceberg in production environments.
  b. Migrate
  The migrate procedure converts an existing Hive table to an Apache Iceberg table while preserving the original table name, schema, and partitioning structure. During the migration process, the source table is temporarily locked and renamed to a backup table, typically using the format BACKUP (or a custom backup name if specified). A new Iceberg table is then created using the original table name. This approach provides a seamless transition to Iceberg while retaining a backup of the original Hive table for recovery or rollback purposes.

Full data migration
Full data migration creates a new Apache Iceberg table by generating the Iceberg metadata layer and rewriting the existing data files from the source table into the new table format. While this approach requires more time and additional compute resources compared to in-place migration, it provides a valuable opportunity to optimize the overall data layout and table design. During the migration process, organizations can enhance table performance by re-sorting data based on common access patterns, implementing Iceberg's hidden partitioning capabilities for more efficient query execution, and converting data into optimized file formats such as Parquet.

Choosing migration strategy based on two migration approaches

Core business use cases for Apache Iceberg:

• Beyond Traditional Data Warehouses: The Lakehouse Evolution
  An Apache Iceberg-based Lakehouse on AWS offers a more flexible and cost-efficient option for traditional data warehouse. By separating storage on Amazon S3 from compute services such as Amazon EMR, AWS Glue, and Amazon Athena, organizations can scale each layer independently and pay only for the resources they consume. This architecture eliminates the need to maintain always-on warehouse clusters while optimizing storage and query costs.

• Transforming Data Lakes into AI-Ready Analytics Platforms
  Apache Iceberg addresses common challenges for data science and machine learning teams such as data drift, reproducibility, and performance at scale through its advanced table management capabilities. Using Iceberg’s time-travel functionality, teams can access and retrain models against the exact historical dataset version used during previous production runs, simplifying model validation, auditing, and root-cause analysis when performance changes occur.
  In addition, Change Data Capture (CDC) pipelines can continuously feed fresh data into Iceberg tables, enabling near real-time feature engineering and analytics workloads. By leveraging merge-on-read optimizations alongside automated compaction processes, organizations can efficiently balance write performance with query speed, ensuring that incremental updates do not impact downstream consumers.

• A Modern Lakehouse Framework for Performance, Governance, and Cost Efficiency
  With AWS Glue Data Catalog as the central metadata repository for Apache Iceberg tables and enforcing fine-grained governance through AWS Lake Formation policies at the database, table, and column levels. Historical and batch datasets can then be ingested using AWS Glue ETL or Apache Spark on Amazon EMR, with a copy-on-write strategy providing predictable performance for scheduled data processing workloads.

Best practices for using Apache Iceberg for AWS Analytics

• Use the AWS Glue Data Catalog as your data catalog
  Use AWS Glue Data Catalog as the primary catalog and maintain a single metadata source for Athena, EMR, Glue, Redshift, and Spark. This will benefit as:

  • Centralized metadata management
  • Better governance
  • Simplified access control

• Use Zstandard (ZSTD) compression
  ZSTD compression codec because it strikes a balance between GZIP and Snappy and offers good read/write performance without compromising the compression ratio. Additionally, compression levels can be adjusted to suit your needs.

• Partition the data
  Iceberg uses partitions as the primary layer of indexing to avoid reading unnecessary metadata files and data files. Column statistics are also taken into consideration as a secondary layer of indexing to further improve query planning.

• Use hidden partitioning
  Hidden Partitioning is one of the key innovations in Apache Iceberg. Unlike Hive-style partitioning, users do not need to explicitly reference partition columns in queries. Iceberg automatically derives partition values from table metadata and optimizes query pruning behind the scenes.

• Partition evolution
  Use partition evolution feature, if existing partition strategy is not optimal. One can use this approach when the best partition strategy for a table is initially unclear and want to refine your partitioning strategy as you gain more insights.

• Choose the right update strategy
  There are two update strategies for update for Apache Iceberg.
  Copy-on-write
  Copy-on-write (COW) optimization prioritizes read performance by writing data files in a read-optimized format, even though it may result in comparatively slower write operations. This approach is particularly effective for analytical workloads where query performance is more critical than update speed. Copy-on-write is best suited for scenarios in which most data modifications are concentrated within the same table partitions, such as periodic batch processing or daily data loads, allowing organizations to achieve faster and more efficient query execution.
  Merge-on-read
  Merge-on-Read (MOR) optimization prioritizes write performance by recording updates and delete operations in separate incremental files rather than immediately rewriting existing data files. When a query is executed, Iceberg dynamically merges these incremental changes with the underlying data files to present the most current view of the dataset.

• Use Schema Evolution Correctly
  Schema Evolution is one of Apache Iceberg's most powerful features. Iceberg allows schema changes as metadata-only operations, eliminating the need to rewrite existing data files while maintaining compatibility with historical data. Iceberg supports adding, dropping, renaming, reordering, and widening column types.

• Run Regular Compaction
  To maintain optimal performance for Apache Iceberg, run compaction regularly to merge small files into larger, appropriately sized files.
  Benefits of Regular Compaction:

  • Improves query performance by reducing the number of files scanned.
  • Minimizes metadata overhead and query planning time.
  • Enhances partition pruning efficiency.
  • Reduces storage and operational costs associated with managing excessive small files.
  • Improves overall table health and scalability.

• Optimize Storage
  Optimize storage for Apache Iceberg tables by implementation of:

  • Enable S3 Intelligent-Tiering
  • Archive or delete historic snapshots

Summary

By combining ACID transactions, schema and partition evolution, time travel, concurrent writes, and high-performance query optimization, Apache Iceberg provides a robust foundation for building scalable, reliable, and efficient data lake and Lakehouse solutions on cloud platforms such as AWS.

Apache Iceberg on AWS enables organizations to build an open, scalable, governed, and AI-ready Lakehouse that supports real-time analytics, historical reporting, machine learning, and multi-engine data access from a single source of truth.