How to build a fast, scalable data system on Azure SQL Database Hyperscale. Hyperscale’s flexible architecture scales with the pace of your business to process large amounts of data with a small amount of compute in just minutes, and allows you to back up data almost instantaneously.

Zach Fransen, VP of data and AI at Xplor, joins Jeremy Chapman to share how credit card processing firm, Clearent by Xplor, built a fast, scalable merchant transaction reporting system on Azure SQL Database Hyperscale. Take a deep dive on their Hyperscale implementation, from their approach with micro-batching to continuously bring in billions of rows of transactional data, from their on-premises payment fulfillment system at scale, as well as their optimizations for near real-time query performance using clustered column store indexing for data aggregation.

► QUICK LINKS:

00:00 - Introduction

00:35 - Intro to Clearent

01:33 - Starting point and challenges

03:12 - Clearant’s shift to Hyperscale

04:53 - Near real-time reporting/micro-batching

06:25 - See it in action

08:28 - Processing large amounts of data

09:42 - Named replicas

10:34 - Query speed ups - clustered column store indexing

11:45 - What’s next for Clearent by Xplor?

12:26 - Wrap up

► Link References:

Learn more about Clearent by Xplor and what they're doing with Hyperscale at https://aka.ms/ClearentMechanics

For more guidance on implementing Azure SQL Database Hyperscale, check out https://aka.ms/MechanicsHyperscale

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