replatforming data warehouse cost cutting initiative

by: Mike Waas

Electronics Manufacturer Reduces Costs By Replatforming to Cloud

Executive Summary

A Fortune 500 Consumer Electronics Manufacturer needed to reduce IT costs, and wanted to take advantage of the features and savings offered by the modern cloud Microsoft Azure™ SQL DW. However, a replatforming effort would be extremely expensive and time consuming: it would take several years and cost tens-of-millions of dollars to rewrite critical business intelligence applications.
This was unacceptable, as their Teradata® licenses were nearly up for renewal, and failing to complete the project in a few months would result in the company incurring massive costs.
Using Datometry Hyper-Q™, the customer was able to replatform their existing business intelligence applications to Microsoft Azure SQL DW in just a few weeks, without having to rewrite any of the applications.


The Fortune 500 Consumer Electronics Manufacturer was facing the following challenges:

  • An imperative to reduce IT costs, and a short timeframe to do so due to impending license expirations
  • An unacceptable level of cost, risk, and time investment required to complete a data warehouse replatforming project and rewrite all mission-critical business intelligence applications.

Datometry’s Solution

  • Running an automated analysis of the application workloads with Datometry qInsight found that the Datometry flagship product 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 Microsoft Azure SQL DW instantly.
  • Hyper-Q enabled their business intelligence applications to communicate natively with Azure SQL DW, allowing the customer to completely replatform their applications several years ahead of schedule.

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 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.