Practical guide to implementing AWS S3 Tables for modern data lake architectures and analytics
Welcome to “AWS S3 Tables for Beginners: Foundation of Modern Analytics” — your complete introduction to one of AWS’s newest and most powerful analytics services.
What you’ll learn
- What AWS S3 Tables are and why they matter in modern analytics.
- How S3 Tables differ from traditional S3 data lakes and Redshift data warehouses.
- The role of Apache Iceberg and how it enables schema evolution, time travel, and ACID transactions.
- How to create and query S3 Tables using Sagemaker Lakehouse, Glue Iceberg Endpoint, S3 Tables Iceberg Endpoint and Catalog.
- How to secure your tables using IAM, Resource Policies and AWS Lake Formation.
- Best practices for managing metadata, compaction, and snapshot cleanup.
Course Content
- Introduction –> 5 lectures • 27min.
- Core Components & Getting Started –> 9 lectures • 35min.
- Accessing S3 Tables –> 3 lectures • 12min.
- Accessing S3 Tables: Using AWS Glue Iceberg Rest Endpoint –> 8 lectures • 32min.
- Accessing S3 Tables: Using S3 Tables Iceberg REST Endpoint –> 7 lectures • 20min.
- Accessing S3 Tables: Using S3 Tables Catalog for Apache Iceberg –> 7 lectures • 22min.
- Security –> 7 lectures • 20min.
- Management –> 8 lectures • 33min.
- Conclusion –> 4 lectures • 11min.
Requirements
Welcome to “AWS S3 Tables for Beginners: Foundation of Modern Analytics” — your complete introduction to one of AWS’s newest and most powerful analytics services.
This course is designed to help you understand, set up, and work with AWS S3 Tables, a modern, open table format built on Apache Iceberg, that brings data warehouse reliability to data lakes.
By the end of this course, you’ll have the skills to create, query, and manage S3 Tables efficiently — and understand how they fit into the broader AWS analytics ecosystem alongside Athena, Redshift, Glue, and Lake Formation.
What You’ll Learn
- What AWS S3 Tables are and why they matter in modern analytics
- How S3 Tables differ from traditional S3 data lakes and Redshift data warehouses
- The role of Apache Iceberg and how it enables schema evolution, time travel, and ACID transactions
- How to create and query S3 Tables using Sagemaker Lakehouse, Glue Iceberg Endpoint, S3 Tables Iceberg Endpoint and Catalog
- How to secure your tables using IAM, Resource Policies and AWS Lake Formation
- Best practices for managing metadata, compaction, and snapshot cleanup
- Hands-on examples of building and accessing S3 Tables via catalogs and APIs
Course Structure
- Introduction – Understand the evolution from data lakes to lakehouses and where S3 Tables fit in
- Getting Started – Learn how to enable and create S3 Tables in your AWS account
- Accessing S3 Tables – Query data using Sagemaker Lakehouse, Glue Iceberg Endpoint, S3 Tables Iceberg Endpoint and Catalog
- Securing S3 Tables – Apply IAM level, Resource level and fine-grained access at table, column, and row-level using Lake Formation
- Managing S3 Tables – Explore maintenance tasks such as compaction, schema evolution, and metadata optimization
- Conclusion – Recap and understand how S3 Tables simplify and modernize data analytics on AWS
Who This Course Is For
- Data engineers, analysts, and cloud architects exploring AWS analytics services
- Professionals transitioning from traditional data warehouses to data lakes or lakehouses
- Anyone who wants to understand how open table formats like Iceberg are changing cloud data management
Prerequisites
- Basic understanding of AWS (S3, IAM, Athena, or Redshift) is helpful but not required
- Awareness on Data Engineering Concepts and Technologies including Data Lake, Open Table Format, Apache Iceberg, Apache Spark and PyIceberg
Why Take This Course
AWS S3 Tables simplify the complexity of self-managed data lakes by automating maintenance, improving query performance, and ensuring data consistency.
With this course, you’ll gain both conceptual clarity and hands-on understanding of how to build reliable, scalable, and open analytics systems on AWS.