aws_immersion_day_new_york_city

Event Full!

Join PingCAP and AWS for an informative, hands-on, in-person workshop!

Time: July 24th, 10 AM – 2 PM ET

Location: AWS New York Office

Address: 7 West 34th Street, New York, NY 10001​

Modern data-intensive applications require an equally modern data stack that can process ever-growing transactional and analytical workloads at scale without provisioning or managing servers.

Join PingCAP and AWS for an in-person workshop where you’ll learn about TiDB Serverless and AWS Lambda. You’ll explore how to combine them to build scalable, highly-available microservices while generating real-time insights directly from raw application data.

Attendees will discover the capabilities and benefits of developing a high volume of transactional data and – as a bonus – analyze that same data set in real time.

Attendees will be given step-by-step instructions on how to reproduce the demo application in a provided AWS environment.

Breakfast and lunch will be available. We’ll also have some free swag on hand for giveaway. 

Who should attend:

Application developers, architects, and DBAs

What you will learn:

  • How to connect TiDB Serverless and AWS Lambda to create a scalable, ACID-compliant application stack that provides real-time analytics on transactional data.
  • How AWS Lambda enables developers to run code without provisioning or managing servers.
  • What problems TiDB Serverless solves as a backend for microservice applications.
  • How TiDB Serverless’ architecture specifically helps with new paradigms of online applications.

Speakers

Ayan Ray

Senior Partner Solutions Architect – Data & Analytics, AWS

Ayan Ray specializes in architecting well-architected solutions with AWS Services and AWS partner products. He has deep expertise in architecting solutions in the field of Data Analytics, Serverless, and Microservices.

Sam Dillard

Principal Product Manager, PingCAP

Sam Dillard is an experienced Product Manager in both the OLTP and OLAP database spaces, with a specialty in distributed systems and data engineering.