How to build and query your first Iceberg Lakehouse on AWS: Hands-on Tutorial

Data and AI driven companies are increasingly turning to iceberg-based lakehouses for their cost-efficiency in storing large datasets and supporting a wide range of data access tools. By choosing Amazon Web Services (AWS) as the cloud platform for deploying these lakehouses, companies benefit from AWS’s robust, scalable, and secure infrastructure, along with its comprehensive suite of integrated services, like Amazon S3, AWS Glue Data Catalog and Amazon Athena.

In this hands-on tutorial, we will guide you through the essentials of setting up, ingesting, and querying an iceberg-based lakehouse on AWS.

We’ll cover:

• Key components and principles behind iceberg-based lakehouses.
• Step-by-step walkthrough to configure your lakehouse storage and catalog.
• Use Upsolver to configure and run an ingestion job to stream events from Amazon Kinesis into the lakehouse.
• Observe the data as it flows in real time, including volume, schema and value distribution. 
• Learn how to evaluate the health and performance of Iceberg tables using the Upsolver table analyzer.
• Query the lakehouse using Amazon Athena.

Watch this practical session and take the first step towards building your own lakehouse in AWS.