Simplify cloud adoption with Azure SQL Data Warehouse and Datometry

We increasingly see that every enterprise is formulating, if not already executing, a cloud-first strategy for their on-premise enterprise data management to benefit from inherent elasticity, flexibility and performance of a cloud data warehouse like Azure SQL Data Warehouse (Azure SQL DW).

The common challenge for moving Azure SQL DW is the complexity of shifting decades of on-premise data management to the cloud . Over years, enterprises have built complex disparate suites of applications such as point of sales, logistics, analytics, and reporting that communicate with a central database. Many apps can’t simply use any database other than the one they were written for originally.

Microsoft has partnered with Datometry to simplify our customer’s journey to the cloud. Re-platforming from Teradata Data Warehouse to SQL DW can be completed in weeks, not years, and at a fraction of the costs compared to traditional migration. With Datometry’s Adaptive Data Virtualization technology, existing Teradata applications can run instantly and natively on Azure SQL DW without rewriting or redesigning the legacy applications.

In the Case Study discussed in this blog post, a Fortune 100 retailer was looking to move their custom business intelligence application with close to 40 million application queries executed per week to Azure SQL DW. This allowed the retailer to take advantage of the performance and security that Azure offers. Their own analysis found rewriting the applications would cost in the tens of millions of dollars and would take multiple years.

Datometry Hyper-Q translates the application queries and results on the fly and doesn’t do any processing of application queries, which means that the heavy computation is left for Azure SQL DW with its MPP architecture. Typically, the translation of application queries takes between five milliseconds and 200 milliseconds. Most of the time customer’s database queries are usually running for seconds or even minutes at a time, so the performance overhead is negligible and often undetectable by monitoring.


In addition, if Hyper-Q encounters functions on stored procedures that are present in the existing database but missing in Azure SQL DW, it emulates them. Stored procedures consist not only of SQL, but of control flow code such as loops. The end result is typically a series of individual statements that are executed against the target database. Hyper-Q unrolls the stored procedures and translates them into individual statements in a syntax that is understood by Azure SQL DW. This allows existing stored procedures to be used even though the target database does not support them.

With this solution, customers can easily accelerate their journey to the cloud while maintaining application up time. Find out more about Azure SQL Data Warehouse and easily get started with a Datometry for Azure SQL Data Warehouse demo.

For anything Azure SQL DW relater question, email and sign up here for engineering support for your Azure SQL DW proof of concept.

Source: Azure Blog Feed

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