replatforming business applications in the cloud

by: Mike Waas

Replatforming Business Applications in the Cloud

Executive Summary

An international Fortune Global 500 Beverage and Brewing Company had a mandate to close their physical data centers and migrate all of their existing applications to the cloud as quickly as possible – ideally in as little as three months. Replatforming Teradata® Data Warehouse applications to the cloud is usually a time-consuming, expensive, risk-laden process and their estimates showed that the project would take approximately five years and cost in the tens of millions of dollars.
Using Datometry Hyper-Q™, the customer was able to run their existing applications natively on the target database of choice, Microsoft Azure SQL DW, in a matter of weeks–without having to rewrite a single line of code.


The Fortune Global 500 Beverage and Brewing Company was facing the following challenges:

  • An enterprise-wide mandate to close their physical data centers and move all business applications to Microsoft Azure™ SQL DW.
  • An unachievable deadline of three months, against an estimated project timeline of five years.

Datometry’s Solution

  • Datometry ran an automated analysis of the application workloads with Datometry qInsight™ found that flagship product Datometry Hyper-Q provided full coverage of the enterprise workloads, removing the need for rewriting the applications.
  • The enterprise simply pointed their existing applications at Hyper-Q as-is and began running mission-critical reports and batch workloads on Azure SQL DW.
  • Hyper-Q enabled the applications to communicate natively with Azure SQL DW in an instant, saving the customer five years and allowing them to complete their project on time while saving tens of millions of dollars.

Why the Data Architect Chose Datometry

Instant Compatibility

Hyper-Q enabled the customer to run their existing applications on the new cloud data warehouse instantly, without rewriting or reconfiguring them.

Fast Deployment & Simple Implementation

Hyper-Q can be deployed instantly and requires a testing phase of just weeks. The software does not require tuning and provides complete visibility into its operations.

High Concurrency & Workload Management Capabilities

Hyper-Q supports high concurrency and workload management capabilities in mixed workloads.

Why the Business Chose Datometry

Reduce Cost of Ownership

The customer was able to leave their expensive, long-standing data warehouse for a more economical data warehouse, cutting CAPEX and OPEX by up to 80%.

Preserve Business Investment

Hyper-Q removes the requirement of rewriting applications—a long, expensive, and risk-laden process for enterprises—thus allowing the customer to protect their long-standing investments in the development of custom business logic.

Accelerated Time to Value

Using Hyper-Q, the customer was able to reduce the time required to replatform by 85.

Decreased Risk

Hyper-Q leaves existing applications unchanged which means projects can be fully tested in advance.

About Adaptive Data Virtualization

Adaptive Data Virtualization technology empowers enterprise IT with the freedom to innovate by unshackling applications from databases and making applications and databases interoperable. The applications can talk directly to Datometry Hyper-Q as if it were the original data warehouse, meaning that rewriting and reconfiguring applications is unnecessary. Once freed from vendor lock-in, the enterprise can innovate rapidly: running and managing applications in cloud databases or data warehouses, within multiple databases or data warehouses, in the cloud, or between different cloud platforms.

About Mike Waas CEO

Mike Waas founded Datometry with the vision of redefining enterprise data management. In the past, Mike held key engineering positions at Microsoft, Amazon, Greenplum, EMC, and Pivotal. He earned an M.S. in Computer Science from the University of Passau, Germany, and a Ph.D. in Computer Science from the University of Amsterdam, The Netherlands. Mike has co-authored over 35 peer-reviewed publications and has 20+ patents on data management to his name.