Mining Your Mainframe Data for More Value

Gil Peleg


Nov 8, 2020

With a global pandemic-induced downturn disrupting economies and whole industries, it has rarely been more important to get “bang for your buck.” Making the most of mainframe data is an excellent example of doing just that. By adopting modern data movement tools, cutting-edge analytics, and low capex cloud resources, organizations can do much more with less – quickly gaining vital insights that can help protect or grow business and/or potentially shaving mainframe costs through reduced MSUs and reduced storage hardware.

Data warehouses were a big step forward when they began to be more widely adopted some 20-30 years ago. But they were expensive and resource-intensive, particularly the extract-transform-load (ETL) process by which disparate and sometimes poorly maintained data was pumped into them.

By contrast, in the same period, data analytics have been undergoing revolution on top of revolution outside of the mainframe world. That’s been particularly so in the cloud where scalability, when needed, is ideal for accommodating periodic or occasional analytic exercises, without incurring heavy capital or operational costs. It is also where some of the most useful analytics tools are at home.

Hadoop, the big data star of recent years, is famous for finding value in even very unstructured data and has helped change the analytic paradigm, which is now rich with AI and machine-learning options for assessing data. Hadoop and other contemporary analytic tools can also digest the kind of structured data that exists in most mainframe applications. So, it would be ideal if one could simply take all that critical mainframe data and let tools like Hadoop look for valuable nuggets hidden within. 

Although technically possible to run Hadoop on Mainframe, most organizations choose to run Hadoop off the MF because of challenges, particularly in the areas of data governance, data ingestion and cost.

In fact, getting mainframe data into Hadoop in a form that can be processed has been very challenging – and expensive. For example, mainframe data could be in EBCDIC form, possibly compressed, rather than the more widely used ASCII. COBOL Copybooks have their own peculiarities as do DB2 and IMS databases and VSAM files.

Fortunately, Model9 has been finding ways to unlock and relocate this badly needed data.  Using an extract-load-transform process that is much faster and easier than ETL (as it doesn’t require mainframe CPU cycles). Model9’s patented technology connects the mainframe directly over TCP/IP to cloud storage chosen by the customer. And it translates all that mainframe data into standard forms, widely used in the cloud. And from there, the analytical choices are numerous.

Best of all, because you can move data back to the mainframe as needed just as easily, Model9 can even eliminate the need for virtual tape libraries and physical tapes.

But the reward that comes from liberating data is probably even more crucial – especially as companies around the globe struggle to make sense of the rapidly changing business conditions and emerging opportunities of 2020 and beyond.

Webinar: Add MF data sets to data analytics w/ Model9 & AWS

About the author

Gil Peleg | CEO
Gil has over two decades of hands-on experience in mainframe system programming and data management, as well as a deep understanding of methods of operation, components, and diagnostic tools. Gil previously worked at IBM in the US and in Israel in mainframe storage development and data management practices as well as at Infinidat and XIV. He is the co-author of eight IBM Redbooks on z/OS implementation.

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