migrate end-of-life Teradata data warehouse

by: Mike Waas

Teradata Data Warehouse – End of Useful Life

Executive Summary

As part of an enterprise-wide mandate to move to the cloud, a Fortune Global 500 Telecommunications Provider was re-evaluating their enterprise data management infrastructure. At the same time, their Teradata® Data Warehouse installation was approaching the end-of-useful life, and they were facing a floor sweep of their data warehouse appliances.

In the course of their research, the customer recognized an opportunity to meet the mandate to modernize IT infrastructure by replacing their existing data warehouse hardware with modern cloud data warehouse technology. However, traditional replatforming projects are prohibitively expensive and time consuming: the customer estimated that it would take approximately 5 years to replatform their applications, and cost an estimated USD 36M.

With Datometry Hyper-Q, the customer was able to replatform to Amazon Redshift™ Data Warehouse in weeks, not years, and at a fraction of the cost usually associated with replatforming.


The Fortune Global 500 Telecommunications Provider was facing the following challenges:

  • An enterprise-wide mandate to modernize IT infrastructure and reduce costs.
  • A floor sweep of existing data warehouse appliances due to end-of-useful life.
  • Their preferred solution was to replatform to Amazon Redshift, but the move would cost them approximately USD 36M, and rewriting the applications would take about 5 years.

Datometry’s Solution

  • Datometry enabled the customer to run their existing applications natively on Amazon Redshift in a matter of weeks, without needing to rewrite or reconfigure them.
  • Datometry automatically generated the schema for the new Amazon Redshift data warehouse, saving the customer months of manual effort.
  • The customer saved 85% of the expected cost of replatforming, and completed their project in just three months instead of the estimated five years.

Why the Data Architect Chose Datometry

Instant Compatibility

Hyper-Q enabled the customer to run their existing applications on the modern 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

Modernize IT Infrastructure

Using Hyper-Q, the customer was able to meet an enterprise-wide mandate to modernize IT infrastructure by taking advantage of cloud technologies.

Decreased Risk

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

Reduce Cost of Ownership

The customer was able to leave their expensive, long-standing data warehouse for a more economical modern data warehouse, cutting CAPEX and OPEX by up to 85%.

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.

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.