Eric Furry


Eric Furry is a Senior Software Consultant at IT Solutions, Inc and is responsible for system architecture and product feature design on a variety of client solutions. He has been working with Microsoft .NET technologies since 2003 and is also an avid lover of Javascript frameworks like Knockout and Angular.

Eric has loved writing software since the age of 12 because it allows you to create something in just a few hours that can then surprise and intrigue you. He also enjoys homebrew beer, cats, and playing the video game Civilization 5.

Scaling Techniques for Azure SQL Database

Saturday, October 22nd, 2016 at 11:30 am in

Have you ever wanted to take an Azure SQL Database that is mostly used from 9-5 PM and automatically scale it up to meet business day demands and scale it down to save money overnight? In this talk we’ll use Postman and also write some code that uses the Azure APIs to do just that, in a technique known as dynamic vertical scaling. We’ll then work with the Azure Elastic Database libraries to “scale-out” and build a more sophisticated sharded database structure that would be suitable to run a large-scale SaaS application.

Don’t worry if you’ve never used Azure SQL Database before—we will introduce Azure SQL Database and work through the key similarities and differences from a typical Microsoft SQL Server installation. For those with experience with SQL Server, it will look and feel very familiar except with some dramatic new capabilities.

Using the Azure Resource Manager API to Scale Azure Database Up and Down on a Schedule

Wednesday, July 20th, 2016 at 5:45 pm

Have you ever wanted to take an Azure Database that is mostly used from 9-5 PM and have it automatically scale up to meet business day demands and scale down to save some money overnight? In this talk we’ll walk through some code that uses the Azure APIs to do just that. This demo uses .NET 4.6.1 and the Azure Active Directory Authentication Libraries (ADAL) to authenticate and then uses the Azure Resource Manager APIs to show how you can automate a change to an Azure Database’s performance level. You can then throw this script into an Azure WebJob and have it run on a schedule that matches performance with demand.

At the end we’ll briefly touch on how this technique of automated vertical scaling can be combined with more formidable horizontal scaling techniques to maximize performance and savings in a variety of scenarios.