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AWS S3 Tables for Beginners: Foundation of Modern Analytics
Published 11/2025
Duration: 3h 32m | .MP4 1280x720 30fps(r) | AAC, 44100Hz, 2ch | 1.36 GB
Genre: eLearning | Language: English
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 withAWS S3 Tables, a modern, open table format built onApache Iceberg, that bringsdata warehouse reliabilitytodata 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 alongsideAthena, 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 ofApache Icebergand how it enables schema evolution, time travel, and ACID transactions
How tocreate and query S3 Tablesusing Sagemaker Lakehouse, Glue Iceberg Endpoint, S3 Tables Iceberg Endpoint and Catalog
How tosecure your tablesusing IAM, Resource Policies and AWS Lake Formation
Best practices formanaging 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 understandhow open table formats like Icebergare 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 bothconceptual clarityandhands-on understandingof 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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