Make Non-Relational Mainframe Data accesible to Modern Analytics Applications

Extending the Value of Mainframe Data

 The vast majority of Fortune 500 companies hold large amounts of critical business data on the mainframe.  Much of this may be stored in relational structures on Db2 for z/OS.  However, there is also a wealth of data stored on non-relational mainframe data sources such as Adabas, IMS, IDMS, VSAM,  and flat files.   Take as an example a bank ATM application, or airline reservation system which may be IMS based.  IMS being a hierarchical vs. relational data store.  This data can be extremely valuable for doing customer analytics.  It has proven to be a challenge to combine all these different data sources using traditional mainframe applications.  Now, add to this the plethora of new data sources being structured, non-structured, on premises, cloud, etc and the challenge can become exponential.  In the past companies have attempted to build data warehouses combining all this data into a single location.  However, the tremendous volumes of data being generated today make the traditional ETL processes too costly and slow.  In order for companies today to gain a competitive advantage they must be able to leverage all these disparate data sources for real-time analysis.  Things like modern business analytics, 360 customer views, and mobile apps cannot afford to have potentially stale data.

Modern applications such as online-shopping, banking, etc are primarily API based.  Leveraging mainframe data in these new applications has been a challenge due to the incompatible formats.  It typically requires costly ETL processes in order to get the data into a recognizable format.  Add to this, the typical programmer today has little or no experience with mainframe can make this an insurmountable task.

Virtualization can be the solution

IBM’s Data Virtualization Manager for z/OS is the only data virtualization tool that runs on the mainframe.  Data Virtualization can make mainframe data much more consumable by providing an abstraction layer that provides real-time read-write access to non-relational mainframe data sources without requiring any mainframe skills.  DVM supports modern APIs such as web services, REST, HTTP, and SOAP allowing developers to easily access mainframe data and join it with other data sources.

IBM Data Virtualization Manager for z/OS architecture

View this short video for a more in-depth understanding of DVM architecture.

Data Virtualization Studio

DVM provides an Eclipse based UI called Data Virtualization Studio.  This interface allows both main-frame and non-framers to easily create virtual tables from non-relational data sources. 

It will also allow the developers to generate code snippets in a broad range of APIs and interfaces:

Easily build virtual views which can join your z/OS data sources with other non-mainframe data sources such as Hadoop, Mongo, Oracle, Db2 LUW, Informix plus much more. 

Click this link for a walkthrough of the Data Virtualization Studio.  you will get experience with the following features:

  • Learn how to define virtual tables
  • Learn how to query virtual tables
  • Learn how to embed virtual tables in application code
  • Learn how to use virtual tables with web services
  • Create views using virtual tables

 

Extending IBM Cloud Pak for Data to the Mainframe

Cloud Pak for Data provides data virtualization for many data sources both relational and non-relational.  Cloud Pak for data runs on a Red Hat OpenShift cluster either on-premise or in the cloud of your choice (IBM Cloud, AWS, Microsft Azure, or Google Cloud). 

Until recently Cloud Pak for Data’s mainframe data was limited to Db2 for z/OS.  DVM is now integrated into Cloud Pak for Data, opening the door to all the other mainframe data sources.  This was a missing link for doing deep analytics that can leverage all this valuable business data.  Developers now have transparent access to non-relational z/OS data sources with the added benefit of Cloud Pak’s collaboration and governance capabilities.  View this video for a brief overview of CP4D’s data virtualization capabilities.

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