Terma Software Announces AutoSys and CA7 Cross-Platform Visualization and Dependency Support

April 25, 2019 9:00 am Published by

While Terma Software’s products have long supported any job scheduler, new functionality streamlines and centralizes the process for companies that utilize multiple scheduling platforms. With this release, companies that use both AutoSys and CA7 in their workload environment can view workload processes across these schedulers as a consolidated single jobstream, or process, using TermaUNIFY. In addition to increased cross-platform visibility, companies now enjoy support for workload dependencies between AutoSys and CA7.

Managing multiple independent job schedulers can be problematic as it requires complex workarounds and inefficiencies. Using TermaUNIFY’s new functionality, CA7 and AutoSys users can now track, predict run times, and initiate reports across schedulers as a single jobstream, simplifying process oversight and providing visibility across the entire workload environment. Terma Software’s intelligence engine will discover and tie two or more processes together whether or not there is a defined dependency between them.

The potential impact on companies that use both CA7 and Autosys is significant. If something goes awry, clients now have the ability to understand the impact on SLAs of jobstreams starting in one platform and ending in another, as well as jobstreams that cross platform or scheduler boundaries. Terma clients have found that increased visibility across multiple job schedulers enable them to precisely pinpoint the exact state of the full environment during a shutdown and quickly resolve issues to prevent and minimize any impact to their customers.

“We’re proud to announce our new cross-platform support for CA7 and AutoSys users. We’re constantly working to develop market-leading functionality that serves our customers’ business needs,” said Terma Software CEO Shane Hade. “Terma’s solutions continue to provide cutting-edge resources for companies seeking actionable intelligence from their workload environments.”

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This post was written by Jim Anderson

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