AWS S3 Tables for Beginners Foundation of Modern Analytics

Practical guide to implementing AWS S3 Tables for modern data lake architectures and analytics
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
Requirements
Basic Understanding of AWS Technologies - AWS S3, AWS IAM
Awareness on Data Engineering - Data Lake, Big Data, Open Table Format, ETL
Awareness on Data Engineering Technologies- Apache Spark, Apache Iceberg, PyIceberg
Description
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 LearnWhat AWS S3 Tables are and why they matter in modern analyticsHow S3 Tables differ from traditional S3 data lakes and Redshift data warehousesThe role of Apache Iceberg and how it enables schema evolution, time travel, and ACID transactionsHow to create and query S3 Tables using Sagemaker Lakehouse, Glue Iceberg Endpoint, S3 Tables Iceberg Endpoint and CatalogHow to secure your tables using IAM, Resource Policies and AWS Lake FormationBest practices for managing metadata, compaction, and snapshot cleanupHands-on examples of building and accessing S3 Tables via catalogs and APIsCourse StructureIntroduction - Understand the evolution from data lakes to lakehouses and where S3 Tables fit inGetting Started - Learn how to enable and create S3 Tables in your AWS accountAccessing S3 Tables - Query data using Sagemaker Lakehouse, Glue Iceberg Endpoint, S3 Tables Iceberg Endpoint and CatalogSecuring S3 Tables - Apply IAM level, Resource level and fine-grained access at table, column, and row-level using Lake FormationManaging S3 Tables - Explore maintenance tasks such as compaction, schema evolution, and metadata optimizationConclusion - Recap and understand how S3 Tables simplify and modernize data analytics on AWSWho This Course Is ForData engineers, analysts, and cloud architects exploring AWS analytics servicesProfessionals transitioning from traditional data warehouses to data lakes or lakehousesAnyone who wants to understand how open table formats like Iceberg are changing cloud data managementPrerequisitesBasic understanding of AWS (S3, IAM, Athena, or Redshift) is helpful but not requiredAwareness on Data Engineering Concepts and Technologies including Data Lake, Open Table Format, Apache Iceberg, Apache Spark and PyIcebergWhy Take This CourseAWS 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.
Who this course is for
Data engineers, analysts, and cloud architects exploring AWS analytics services
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