SHARE Providence Recap: IBM z14, ITOA, ITSI, SIEM & Machine Learning

SHARE Providence Recap: IBM z14, ITOA, ITSI, SIEM & Machine Learning

Being a Floridian, I must admit that by August I do like to escape to an area that is just a little cooler and less humid than my home state. And even though I had to shorten my time at SHARE Providence to two days, it was still wicked awesome for me!

SHARE Providence: Old Colleagues, New Trends

I can’t believe how many old colleagues and friends I was able to catch up with at the event. We all said how good it was to see each other and how great we looked… and I almost believed it until I got back to my hotel room and looked in the mirror and realized what a bunch of lying fools we have become!

Heck, I have known some of these people for 30+ years, more than half my life… and there is no way we look like did back in the “early days.” As my father used to say, may he rest in peace, “I’m in great shape for the shape I am in.” And that about sums it up.

Speaking of the early days, I spent a good portion of my career in performance and availability management working on OMEGAMON performance monitors at Candle Corporation, which is now part of IBM. In meeting-up with my old friends from there I quickly realized that we have all evolved down similar paths.

Download our eBook: Mainframe and Machine Learning for IT Service Intelligence (ITSI)

In those early days working on performance monitors, we were all focused on identifying and resolving problems that were impacting systems and applications. We were concerned about preventing unscheduled Initial Program Loads (IPLs) and outages.

Fortunately, systems and applications have become much more stable and IPL prevention is somewhat of a dead art. However, the data that we had to access and evaluate in real-time to prevent the outages led us all down this newer path of “Big Data Analytics.”

The Convergence of Big Data and Mainframe Initiatives

Big Data Analytics is about leveraging z/OS machine data for IT Operational Analytics (ITOA), IT Service Intelligence (ITSI), and Security Information and Event Management (SIEM). In most cases, critical z/OS data sources like SMF records, RMF data, and USS log files are being forwarded in near real-time from mainframe to analytics platforms including Splunk, Elastic (ELK) and Hadoop to support critical analysis.

The star of SHARE Providence: IBM's new z14

IBM’s new z14 was the star of SHARE Providence, but security and compliance, machine learning & Big Data Analytics for ITOA, ITSI and SIEM were hot topics as well

Even my colleagues at IBM have seen the light regarding off-platform analytics. They used to talk about moving the analytics to the data – i.e. bring analytics to the mainframe, but now they are talking about offloading the data for better analytics.

We are off-loading and using this z/OS data to achieve better operational efficiency to ensure we are getting the most out of our existing IT resources. This includes discovering opportunities to offload General Processor cycles to zIIP engines to manage capacity and delay CPU upgrades; ensuring better delivery of IT services supporting critical business processes; identifying and preventing security threats; and addressing audit requirements to meet compliance initiatives.

I delivered a session on this in Providence titled: “Old Dogs, New Tricks: Big Data from and for Mainframe IT.” If you missed it, I am going to deliver a repeat performance via WebEx on Tuesday August 22nd at 11AM Eastern time. Click here to register and attend.

SHARE Providence 2017: Syncsort's Maggie Li presents on mainframes and machine learning

SHARE Providence 2017: Syncsort’s Maggie Li presents on mainframes and machine learning

SHARE Providence was indeed wicked awesome! There was a lot to discuss around the new IBM z14, enhancements in data protection facilities in z/OS, and the future of IBM Z in general. We also talked about security and regulatory compliance concerns that impact z/OS professionals, as well as opportunities for ongoing optimization of our z/OS systems.

My colleague Maggie Li, our Chief Architect for mainframe, presented a session titled: “Mainframe, Meet Machine Learning” discussing how machine learning technologies can provide benefits to our z/OS practices. To learn more, you can read our brand new eBook Mainframe and Machine Learning for IT Service Intelligence.

If you attended SHARE, I hope you had a chance to meet Syncsort’s IBM Z Experts and discuss how our mainframe optimization and Big Iron to Big Data solutions helps reduce mainframe costs by processing more data in less time with fewer resources, and harnesses valuable data assets for advanced Big Data analytics on next-generation platforms like Splunk, Hadoop and Spark.

Ed Hallock

Authored by Ed Hallock

Ed Hallock is the Director of Product Management for Ironstream at Syncsort. He is a highly experienced Information Technology Professional with a broad experience base in software product development, support, product management, marketing, and business development. In his diverse career Ed has benefited from working for some of the largest independent software vendors, in a variety of roles, providing enterprise solutions to Global 1000 corporations.

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