Over the past 10 years we’ve seen an explosion of data-intensive applications, from streaming content to advanced analytics to mobile commerce, driving a requirement to tap into massive stores of data on enterprise systems. This puts tremendous stresses on these systems as data is moved to these cloud-based applications, creating inefficiencies, bottlenecks and security concerns.
But with the recent introduction of Rocket Data Virtualization (DV), we’re turning that model on its head – instead of moving the data to the application, we’re now moving the application to the data. This enables data structures that were designed independently to be used together, from a single source, in real time, and without data movement.
In essence, the rules of game have changed as mobile, analytics, cloud and volume data consumers want innovative solutions and fast access to business information. Virtualization of mainframe data provides these applications and users with a single entry point into core business data.
With Rocket DV and Rocket Mainframe Data Service along with MongoDB, Rocket Java Database Connectivity (JDBC), and applications on IBM Bluemix, users gain real-time, universal access to mainframe data regardless of location or format, without moving or replicating the data off-host. Rocket DV mapping of non-relational data sources provides full meta discovery just like relational data sources provide, guaranteeing users that access to business data is the same regardless of access methods used.
Using DV MapReduce and Query Optimization provides parallel access to these data sources. And the DV client provides legacy applications with the same “map once/use many” view of business data. As an added benefit, this also gives legacy z Systems applications access to the same data sources available to MongoDB and Rocket JDBC and Open Database Connectivity (ODBC).
This approach delivers mainframe data in relational format compatible with business intelligence and analytics applications. It can also allow data virtualization to create views of the data source, which provides access to multiple z Systems data sources via the Mongo API. DV MapReduce exploits the patented hybrid Service Request Block/Task Control Block paradigm, which provides up to 99-percent offload to IBM z Integrated Information Processor specialty engines, freeing z Systems general processors to serve business applications.
What this all results in is faster access to data for cloud-based applications, higher efficiency, greater data security, and a lower total cost of ownership.