Stream: MQ & AppDev II
Time: 15:15 - 16:00
In order to evolve your mainframe applications, you need to fully understand them inside and out ... with solutions that span application and data intelligence. But this needs a wider lens than just the application code. It requires knowledge around the entire mainframe ecosystem, including batch schedulers, subsystems, third-party utility languages, and other technologies.
However, questions that arise that underpin this task are many, varied and complex.
What is the full list of application assets and artefacts that should be under consideration as part of a full mainframe application portfolio review? How complex is this application suite? How does this application technology integrate with open-source DevOps pipelines? How much time is spent looking for data that can be trusted? How fast can you find out where the data is coming from and where it is going?
And importantly, what does the actual mainframe and data landscape look like at a high-level?
This session will seek to answer these, and many more questions.
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