Posts Tagged ‘agile’

Compuware Continues Mainframe Software Renaissance

January 19, 2017

While IBM focuses on its strategic imperatives, especially cognitive computing (which are doing quite well according to the latest statement that came out today–will take up next week), Compuware is fueling a mainframe software renaissance on its own. It’s latest two announcements brings Java-like unit testing to COBOL code via its Topaz product set and automate and intelligently optimize the processing of batch jobs through its acquisition of MVS Solutions. Both modernize and simplify the processes around legacy mainframe coding thus the reference to mainframe software renaissance.

compuware-total-test-graphic-process-flow-diagram

Let’s start with Compuware’s Topaz set of graphical tools. Since they are GUI-based even novice developers can immediately validate and troubleshoot whatever changes, either intended or inadvertent, they made to the existing COBOL applications.  Compuware’s aim for Topaz for Total Test is to eliminate any notion that such applications are legacy code and therefore cannot be updated as frequently and with the same confidence as other types of applications. Basically, mainframe DevOps.

By bringing fast, developer-friendly unit testing to COBOL applications, the new test tool also enables enterprises to deliver better customer experiences—since to create those experiences, IT needs its Agile/DevOps processes to encompass all platforms, from the mainframe to the cloud.  As a result z shops can gain increased digital agility along with higher quality, lower costs, and dramatically reduced dependency on the specialized knowledge of mainframe veterans aging out of the active IT workforce. In fact, the design of the Topaz tools enables z data centers to rapidly introduce the z to novice mainframe staff, which become productive virtually from the start—another cost saver.

Today in 2017 does management still need to be reminded of the importance of the mainframe. Probably, even though many organizations—among them the world’s largest banks, insurance companies, retailers and airlines—continue run their business on mainframe applications, and recent surveys clearly indicate that situation is unlikely to change anytime soon. However, as Compuware points out, the ability of enterprises to quickly update those applications in response to ever-changing business imperatives is daily being hampered by manual, antiquated development and testing processes; the ongoing loss of specialized COBOL programming knowledge; and the risk and associated fear of introducing even the slightest defect into core mainframe systems of record. The entire Topaz design approach from the very first tool, was to make mainframe code accessible to novices. That has continued every quarter for the past two years.

This is not just a DancingDinosaur rant. IT analyst Rich Ptak from Ptak Associates also noted: “By eliminating a long-standing constraint to COBOL Compuware provides enterprise IT the ability to deliver more digital capabilities to the business at greater speed and with less risk.”

Gartner in its latest Predicts 2017, chimes in with its DevOps equivalent of your mother’s reminder to brush your teeth after each meal: Application leaders in IT organizations should adopt a continuous quality culture that includes practices to manage technical debt and automate tests focused on unit and API testing. It should also automate test lab operations to provide access to production-like environments, and enable testing of deployment through the use of DevOps pipeline tools.” OK mom; everybody got the message.

The acquisition of MVS Solutions, Compuware’s fourth in the last year, adds to the company’s collection of mainframe software tools that promise agile, DevOps and millennial-friendly management of the IBM z platform—a continuation of its efforts to make the mainframe accessible to novices. DancingDinosaur covered these acquisition in early December here.

Batch processing accounts for the majority of peak mainframe workloads at large enterprises, providing essential back-end digital capabilities for customer-, employee- and partner-facing mobile, cloud, and web applications. As demands on these back-end mainframe batch processes intensify in terms of scale and performance, enterprises are under increasing pressure to ensure compliance with SLAs and control costs.

These challenges are exacerbated by the fact that responsibility for batch management is rapidly being shifted from platform veterans with decades of experience in mainframe operations to millennial ops staff who are unfamiliar with batch management. They also find native IBM z Systems management tools arcane and impractical, which increases the risk of critical batch operations being delayed or even failing. Run incorrectly, the batch workloads risk generating excessive peak utilization costs.

The solution, notes Compuware, lies in its new ThruPut Manager, which promises automatic, intelligent optimized batch processing. In the process it:

  • Provides immediate, intuitive insight into batch processing that even inexperienced operators can readily understand
  • Makes it easy to prioritize batch processing based on business policies and goals
  • Ensures proper batch execution by verifying that jobs have all the resources they need and proactively managing resource contention between jobs
  • Reduces the organizations’ IBM Monthly Licensing Charges (MLC) by minimizing rolling four-hour average (R4HA) processing peaks while avoiding counter-productive soft capping

Run in conjunction with Strobe, Compuware’s mainframe application performance management tool, ThruPut Manager also makes it easier to optimize batch workload and application performance as part of everyday mainframe DevOps tasks. ThruPut promises to lead to more efficiency and greater throughput resulting in a shorter batch workload and reduced processing capacity. These benefits also support better cross-platform DevOps, since distributed and cloud applications often depend on back-end mainframe batch processing.

Now, go out an hire some millenials and bring fresh blood into the mainframe. (Watch for DancingDinosaur’s upcoming post on why the mainframe is cool again.)

DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.

 

IBM Continues Open Source Commitment with Apache Spark

June 18, 2015

If anyone believes IBM’s commitment to open source is a passing fad, forget it. IBM has invested billions in Linux, open Power through the Open Power Foundation, and more. Its latest is the announcement of a major commitment to Apache Spark, a fast open source and general cluster computing system for big data.

spark VGN8668

Courtesy of IBM: developers work with Spark at Galvanize Hackathon

As IBM sees it, Spark brings essential advances to large-scale data processing. Specifically, it dramatically improves the performance of data dependent-apps and is expected to play a big role in the Internet of Things (IoT). In addition, it radically simplifies the process of developing intelligent apps, which are fueled by data. It does so by providing high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

IBM is contributing its breakthrough IBM SystemML machine learning technology to the Spark open source ecosystem. Spark brings essential advances to large-scale data processing, such as improvements in the performance of data dependent apps. It also radically simplifies the process of developing intelligent apps, which are fueled by data. But maybe the biggest advantage is that it can handle data coming from multiple, disparate sources.

What IBM likes in Spark is that it’s agile, fast, and easy to use. It also likes it being open source, which ensures it is improved continuously by a worldwide community. That’s also some of the main reasons mainframe and Power Systems data centers should pay attention to Spark.  Spark will make it easier to connect applications to data residing in your data center. If you haven’t yet noticed an uptick in mobile transactions coming into your data center, they will be coming. These benefit from Spark. And if you look out just a year or two, expect to see IoT applications adding to and needing to combine all sorts of data, much of it ending up on the mainframe or Power System in one form or another. So make sure Spark is on your radar screen.

Over the course of the next few months, IBM scientists and engineers will work with the Apache Spark open community to accelerate access to advanced machine learning capabilities and help drive speed-to-innovation in the development of smart business apps. By contributing SystemML, IBM hopes data scientists iterate faster to address the changing needs of business and to enable a growing ecosystem of app developers who will apply deep intelligence to everything.

To ensure that happens, IBM will commit more than 3,500 researchers and developers to work on Spark-related projects at more than a dozen labs worldwide, and open a Spark Technology Center in San Francisco for the Data Science and Developer community to foster design-led innovation in intelligent applications. IBM also aims to educate more than 1 million data scientists and data engineers on Spark through extensive partnerships with AMPLab, DataCamp, MetiStream, Galvanize, and Big Data University MOOC (Massive Open Online Course).

Of course, Spark isn’t going to be the end of tools to expedite the latest app dev. With IoT just beginning to gain widespread interest expect a flood of tools to expedite developing IoT data-intensive applications and more tools to facilitate connecting all these coming connected devices, estimated to number in the tens of billions within a few years.

DancingDinosaur applauds IBM’s decade-plus commitment to open source and its willingness to put real money and real code behind it. That means the IBM z System mainframe, the POWER platform, Linux, and the rest will be around for some time. That’s good; DancingDinosaur is not quite ready to retire.

DancingDinosaur is Alan Radding, a veteran IT analyst and writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing on Technologywriter.com and here.


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