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DVM and the Hybrid cloud - where the mainframe meets the cloud

By Doug Dailey posted Sat April 30, 2022 08:04 AM


DVM supports IBM core data strategy that offers the flexibility needed for today's hybrid cloud environments. We all know about DVM's BIC capabilities for differentiated access to data residing on the Z platform. We also know that DVM offers a client JDBC Gateway server for outbound access to remote stores like Teradata, Oracle, SQL Server, Postgres, and Db2, among others. But we usually frame DVM in the context of on-premises environments. But did you know that DVM supports usage in the cloud? 

This means that you are able to bridge both legacy and traditional business critical environments primarily living on-premises with cloud stores. The traditional use case for federation technology was the typical bank merger where a larger banking or finance organization acquires smaller regional banks to expand its footprint for growth. Federating technology allowed for banks to virtualize and establish semantic models that shared data without compromising on quality, access, and latency. While the larger bank worked toward standardization, common processes, they were able to maintain an equivalent level of service for their customers while maintaining a single common view of all data. 

Over time, the acquired bank systems migrated to the primary banking system and in some cases remained intact and accessible through virtual tables, views, or by way of an alias from the primary databases for business applications to have 24/7 continuous availability to data. This data integration strategy remains a common practice as organizations look to leverage the cloud to provide new services for B2B and B2C and simply shift from capex to opex for cost savings. The cloud continues to be a north star for business strategy and businesses are looking to selectively derive more value and add to their bottom line through the cloud.

DVM can not only service this primary use case but provide access to data for a variety of other uses, such as copying subsets of mainframe data to cloud stores using ETL tooling, accessing mainframe data from a RESTful application running natively on the cloud (SaaS), as well as mainframe Cobol applications accessing remote data living in the cloud. In addition to these uses, DVM also supports many modern data platforms, such as IBM's Cloud Pack for Data, built on top of RedHat OpenShift and a Kubernetes framework that helps to orchestrate a wide range of services covering governance, data collection, analytics, ML/AI and more.

DVM has been prototyped for use with SPSS Modeler for building ML training, which can combine with AI to help provide data-driven insights to make actional decisions for your business. It has been used for connecting popular 3rd party reporting and analytics solutions such as Power BI and has connected with leading ETL tooling like IBM Data Stage, and competitors like Informatica and Splunk to extract business critical information for placement in the cloud for more agile development and deployment.

To get an idea of how DVM can be used with IBM's Cloud Pack for Data, take a look at this short video that highlights a hybrid cloud environment that includes data on Z, IBM's Cloud Pack for Data running as a private cloud, and Power BI running from a workstation or public cloud environment. Now is the time to begin to incorporate active and critical data running on your mainframe into IBM's exciting data fabric.