Build fast, scalable data system on Azure SQL Database Hyperscale | Clearent

published 1 month ago by Microsoft Mechanics

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 For more guidance on implementing Azure SQL Database Hyperscale, check out ► Unfamiliar with Microsoft Mechanics? We are Microsoft’s official video series for IT. You can watch and share valuable content and demos of current and upcoming tech from the people who build it at #Microsoft. Subscribe to our YouTube: Join us on the Microsoft Tech Community: Watch or listen via podcast here: ► Keep getting this insider knowledge, join us on social: Follow us on Twitter: Follow us on LinkedIn: Follow us on Facebook:

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