Allyn Henderson

Agentic AI applications are only as good as the data supporting them, and that data often lives across on-premises systems, other clouds, and distributed AWS storage.

Data protection strategies today must span workloads and prove recovery works before an incident demands it. In this workshop, you'll implement recovery and data protection strategies for Amazon S3, Amazon EFS, and Amazon EBS with AWS Backup.

Understanding which data to migrate—and why—is crucial for a successful migration to AWS. In this chalk talk, we’ll explore the process of data discovery and workload analysis to drive accurate, workload-aligned migration planning.

This workshop showcases the advanced data protection capabilities of AWS Backup, focusing on AWS Backup logically air-gapped vaults, AWS Backup restore testing, and AWS Backup Audit Manager. Work through the entire backup lifecycle while implementing robust, compliant backup strategies.

Set up efficient data pipelines from on-premises systems, other clouds, and third-party sources to Amazon S3 using AWS Storage Gateway, DataSync, and Transfer Family. This hands-on workshop demonstrates how to integrate migrated data with Amazon Bedrock for generative AI processing and analysis.

Learn how to ingest data into Amazon S3 from on premises, other clouds, and third-party sources using AWS Storage Gateway, AWS DataSync, and AWS Transfer Family. This hands-on workshop guides you through setting up data pipelines and utilizing Amazon Q to parse and analyze your data with generative AI.

Learn about a great way to take advantage of AWS AI/ML and compute technologies when your data is created on premises. In this workshop, build a data pipeline to write your data to Amazon S3 File Gateway on premises through SMB or NFS, upload it to Amazon S3 in the cloud, and process it using AWS compute resources.

Join this builders’ session to immerse yourself in the world of multi-cloud and on-premises data transfers. Learn how to configure and perform a data transfer from an on-premises NFS server and a publicly accessible Google Cloud Storage bucket that is hosting a public dataset to Amazon S3.

What is the best way to take advantage of AWS AI, ML, and compute technologies when your data is created on premises? In this workshop, build a data pipeline to write your data to Amazon S3 File Gateway on premises through SMB or NFS, upload it to the cloud, and process it using AWS compute resources.