Model9, a US-based cloud data management service provider partnered with Pure Storage and releases a joint solution to modernize the method of mission-critical data management by mainframe enterprises.

This joint solution will track the market requirement for a combined cloud-based data management and data protection solution for the mainframe, which is as of now restricted to legacy storage solutions in the mainframe environment that keep data inaccessible to cloud services.

Ransomware protection, data management, recovery as well as enablement of integration of AI, ML, and analytics services with mainframe data through object storage will be delivered by the joint solution.

Chad Monteith, Principal Architect, Field Solutions & Strategy, Office of the CTO, Pure Storage, commented, “Historically, mainframes have been isolated islands of data with limited connectivity to the services and applications in modern data centers. The joint Model9 and Pure Storage solution modernize the mainframe with connectivity to open systems platforms, enabling modern, cloud-like economics and application workflows, and bringing a more effective data consumption model to enterprise customers.”

Model9 Manager executes archiving, backup, disaster recovery, and space management right from the mainframe to FlashBlade. FlashBlade is Pure Storage’s unified fast file and object storage solution. Model9 shield creates copies of data on FlashBlade and also protects them.

Companies air-gap the data as the resulting data gets compressed and encrypted end-to-end. Air-gap is the procedure of additional copy isolation from the network to remove potential cyberattack exposure.

So, if a cyberattack happens, data can be refixed due to the availability of the backup, and FlashBlade’s functionality and performance get restored which is merged with backup reduction and elapsed time restoration abilities of Model9 and improves output respectively.

As soon as the data gets stored on FlashBlade, Model9 transforms the data to any open format without the consumption of the mainframe CPU. Analytics, AI, and ML applications can come into action to control both historical and current mainframe data to derive better business insights.