Mastering Analytical Data Lake : Amazon S3 tables(New)

In this session, you will learn on how to set policies for accessing the S3 tables and for practice sessions

You’ll learn how S3 Tables empower administrators with high-performance, managed tabular storage, including automated maintenance and built-in Apache Iceberg support.

We’ll explore how access management works for S3 Tables, using dedicated IAM and resource-based policies for granular security at multiple levels, with public access blocked by default.

Details the main building blocks of S3 Tables, including how data is organized and managed within buckets.

Introduces the APIs available for interacting with S3 Tables, covering basic operations and integration points.

In this session, introduce the audience to table operation and CLIs

Step-by-step guide to creating a new S3 Table bucket and preparing it for analytics workloads.

Shows how to organize data using namespaces and create tables within S3 Tables.

Demonstrates how to run SQL-like insert, update, and delete operations on S3 Tables using Athena.

Explains how to register S3 Tables with LakeFormation, enabling fine-grained access control and data governance.

Walks through the process of creating S3 Tables directly from LakeFormation, ensuring compliance and security best practices.

Provides a practical demonstration of how to use S3 Tables as a data source for SageMaker Lakehouse, supporting ML and analytics scenarios.

In this session you will learn how to interact with S3 tables from AWS Glue notebooks.

Covers methods and best practices for streaming data directly into S3 Tables, enabling up-to-date analytics and reporting.

This session will guide you through streaming real-time data from Amazon Kinesis Data Streams into S3 tables using Amazon Data Firehose, seamlessly integrating with AWS Lake Formation for secure data governance. You’ll learn how to configure Firehose to deliver streaming data directly into S3Tables-based Apache Iceberg tables, making the data instantly queryable with analytics services like Amazon Athena and AWS Glue. By the end, you’ll understand how to build a modern data lake architecture for real-time analytics, leveraging Kinesis for ingestion and S3 tables for scalable, governed storage and analysis

This session covers the architecture for integrating Amazon S3 Tables with QuickSight using AWS Lake Formation and Athena. We’ll explain why direct connectivity from QuickSight to S3 Tables isn’t possible, and how Lake Formation acts as a governance layer—registering S3 Table buckets, managing permissions, and brokering secure access for analytics services like Athena. You’ll learn how SAML-based identities and the QuickSight service role are mapped in Lake Formation, ensuring only authorized users and services can query and visualize S3 Table data in QuickSight, all while maintaining centralized, fine-grained access control

In this session, we'll explore how to connect Amazon S3 Tables to QuickSight using AWS Lake Formation as a secure intermediary, since direct integration isn't available yet. We'll break down the critical roles of Lake Formation permissions, service-linked roles, and SAML-based access management for federated users. By the end, you'll understand how to securely visualize S3 Table data in QuickSight while maintaining fine-grained access control.

In this sesssion, we talk about the S3 table pricing and pricing based on a scenario.

In this session, you will learn the best practices and roadmap to onboard S3 tables