Seamless Analytics with Amazon Redshift and Apache Iceberg on Amazon S3

Introduction

In today’s digital era, organizations are generating data at an unprecedented rate. Modern enterprises are increasingly shifting to cloud-native architectures that offer flexibility, scalability, and cost-efficiency to keep up. While data lakes have become the de facto standard for storing massive amounts of raw data, managing large-scale tabular datasets efficiently and effectively within distributed environments still presents considerable challenges, particularly around performance, governance, and operational complexity.

Enter Apache Iceberg, Amazon S3 Tables, and Amazon Redshift. Together, these technologies offer a transformative solution that enables organizations to query Iceberg-formatted tables stored in Amazon S3 directly from Amazon Redshift, without the need for data duplication or complex ETL processes. This integration streamlines data analytics workflows and supports real-time insights using a serverless, scalable model.

This blog explores setting up and leveraging this integration to enhance your analytics capabilities while simplifying infrastructure management.

Pioneers in Cloud Consulting & Migration Services

Reduced infrastructural costs
Accelerated application deployment

Get Started

Apache Iceberg

Apache Iceberg is a high-performance, open table format tailored for huge analytical datasets in cloud-native environments. Built with modern data lake needs in mind, Iceberg supports:

ACID transactions
Schema evolution
Hidden partitioning
Time-travel queries

Iceberg simplifies and strengthens large-scale analytics operations, enabling organizations to maintain reliability and data consistency without compromising performance.

Amazon S3 Tables

Amazon S3 Tables offer a fully managed service for storing and managing Iceberg tables directly on Amazon S3. These tables support high-volume analytical workloads and can be queried directly using services like Amazon Athena and Amazon Redshift, eliminating the need to move or duplicate data across platforms.

Amazon Redshift

Amazon Redshift is a managed data warehouse that delivers high-speed analytics at scale. With Redshift Spectrum, you can extend Amazon Redshift’s querying capabilities to data stored externally, in this case, in Amazon S3, by using Apache Iceberg-formatted tables. This provides a seamless bridge between data lakes and warehouses, facilitating unified analytics without additional data pipelines.

Step-by-Step: Setting Up the Integration

Step 1: Create a Table Bucket in Amazon S3

Log into the Amazon S3 console and create a bucket configured to store tabular data in Iceberg format. This table bucket supports metadata-driven querying and integration with analytics services. Enable analytics integration to allow services like Amazon Redshift to access and read data efficiently.

Step 2: Register the Bucket in AWS Lake Formation

Navigate to AWS Lake Formation, a centralized metadata and governance layer. Register the newly created table bucket to make it discoverable by Redshift and other AWS services. Assign the required AWS IAM roles and permissions to ensure secure access to the data and metadata.

Step 3: Load Data into Iceberg Tables

Once the bucket is registered, populate it with data using one of several ingestion methods:

Amazon Athena for SQL-based batch inserts
Amazon Kinesis Data Firehose for real-time streaming ingestion
Apache Spark or Amazon EMR for large-scale transformations and writes

Regardless of your ingestion method, the Iceberg table format will handle partitioning and schema evolution in the background.

Step 4: Query Iceberg Tables from Amazon Redshift

With your data lake set up, move to the Amazon Redshift console and perform the following:

Create an external schema that links to your AWS Glue Data Catalog, where your Iceberg tables are registered.
Use standard SQL queries to query the data as if it resided within Amazon Redshift.

Due to Amazon Redshift Spectrum, Amazon Redshift reads the data directly from Amazon S3, leveraging performance features such as metadata pruning, columnar storage, and predicate pushdown to deliver fast, efficient results.

apache

Key Advantages of the Integration

No ETL Required

This setup eliminates the need for data extraction, transformation, or loading. You can query Iceberg tables directly in place, reducing data movement, time-to-insight, and pipeline complexity.

Built-In Performance Enhancements

Iceberg tables in Amazon S3 benefit from optimizations like column pruning and metadata caching, significantly improving query performance. You scan less data, reduce latency, and save costs.

Unified Data Governance

With AWS Lake Formation managing access policies, you ensure consistent security and governance across your data warehouse and lake. This unified approach helps meet compliance requirements without extra configuration.

Schema Flexibility and Time Travel

Iceberg’s built-in support for schema evolution means you can change your table structures over time without breaking queries or pipelines. Additionally, its time-travel capabilities let you query historical data snapshots, which is useful for audits and rollback scenarios.

Conclusion

Integrating Apache Iceberg, Amazon S3 Tables, and Amazon Redshift marks a significant leap in the evolution of modern data architectures. It combines the flexibility and scalability of data lakes with the performance and simplicity of data warehouses, without introducing operational overhead or cost inefficiencies.

With this setup, organizations can unlock powerful, serverless analytics capabilities, enabling data teams to query massive datasets in real-time, apply consistent governance, and adapt quickly to changing business needs. Whether modernizing your platform or building a new cloud-native analytics stack, this solution offers a future-ready foundation.

Drop a query if you have any questions regarding Apache Iceberg and we will get back to you quickly.

Empowering organizations to become ‘data driven’ enterprises with our Cloud experts.

Reduced infrastructure costs
Timely data-driven decisions

Get Started

About CloudThat

CloudThat is a leading provider of Cloud Training and Consulting services with a global presence in India, the USA, Asia, Europe, and Africa. Specializing in AWS, Microsoft Azure, GCP, VMware, Databricks, and more, the company serves mid-market and enterprise clients, offering comprehensive expertise in Cloud Migration, Data Platforms, DevOps, IoT, AI/ML, and more.

FAQs

1. Can I write or update Iceberg tables from Amazon Redshift?

ANS: – Not at this time. Amazon Redshift supports read-only access to Iceberg tables. For insert, update, or upsert operations, consider using Amazon Athena, Amazon EMR, or Apache Spark, which support write functionality.

2. Are there additional costs for querying Iceberg tables from Amazon Redshift?

ANS: – There is no additional fee for the Iceberg integration itself. However, Amazon Redshift Spectrum pricing applies based on the amount of Amazon S3 data scanned during queries. Costs can be controlled by: