disaster recovery data warehouse

by: Mike Waas

Disaster Recovery Cloud Data Warehouse

Executive Summary

A U.S. Fortune 100 Healthcare Provider realized an urgent necessity for a Disaster Recovery (DR) system for its enterprise data warehouses. They wanted the destination to be in the cloud and needed to significantly reduce CAPEX and OPEX. The requirement that the existing applications on their Teradata® Data Warehouse communicate seamlessly with both the on-premise data warehouse and the Amazon Redshift™ disaster recovery data warehouse they had selected was an insurmountable obstacle for the business. Using Datometry Hyper-Q™, the customer was able to set up a disaster recovery system in the cloud without having to rewrite any of their existing analytic and ETL applications.


The Healthcare Provider was facing the following challenges:

  • An urgent necessity for a disaster recovery data warehouse system, which needed to be cloud-native to take advantage of the latest technology and significantly reduce CAPEX and OPEX.
  • The customer’s existing applications needed to communicate seamlessly with the Amazon Redshift data warehouse, and facilitating this communication was believed to be an insurmountable roadblock.

Datometry’s Solution

  • Datometry ran a quick automated analysis of the customer’s application workloads using Datometry qInsight™ and found that its flagship product Datometry Hyper-Q provided full coverage of the customer’s BI and ETL workloads, removing the need for rewriting the applications.
  • Datometry generated the schema for the new disaster recovery data warehouse automatically, using its Datometry qShift™ schema synthesizer product.
  • Hyper-Q enabled applications to communicate natively with the existing data warehouse and Amazon Redshift during a failover situation.

Why the Data Architect Chose Datometry

Enabling Disaster Recovery Architecture

Hyper-Q enabled the customer to instantly run its existing analytic applications and ETL on a new Disaster Recovery cloud data warehouse.

Instant Compatibility

Hyper-Q enabled the customer to set up their existing applications on the new cloud disaster recovery 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 set up an economical disaster recovery in the cloud with CAPEX and OPEX savings of up to 80%.

Decreased Risk

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

Accelerated Time to Value

Using Datometry Hyper-Q, the customer was able to reduce the time required to set up a cloud disaster recovery data warehouse by 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.