Mainframe logs collection can be a complex task. There are too many logs, the SCRT report isn’t specific enough, the time commitment is simply too exhausting — there are countless operational obstacles that prevent IT teams from realizing the full potential of mainframe data analytics. Pervasive challenges such as these leave most IT teams with unfulfilled performance goals and an unsustainable budget.
Relevant, specific, and up-to-date data — these are the three criteria necessary for performing mainframe optimization. And yet, what seems fundamental in theory is actually nearly impossible to gather in practice. Why is it so hard to collect actionable mainframe data?
Let’s break down the institutional obstacles that hinder effective data collection, and learn how the latest analytics software helps reinvent the way mainframe managers optimize system performance.
The Challenges of Collecting Mainframe Logs
Mainframes produce a lot of logs. And we do mean a lot. Mainframes generate billions of logs every day. Of course, this is an insurmountable amount of information for any team team to sift through. As a result, mainframe managers struggle to retrieve anything useful, or actionable, from mainframe logs due to sheer volume.
Mainframe managers are really only looking for three qualities in their mainframe data, but they end up lost in a mix of complexities. In order to perform successful logs analysis, your IT team needs the following:
- Relevant data: Inundated in endless logs, it can be hard to extract any meaningful insight. By default, there is no easy way to capture job-relevant data, and this leaves IT professionals to painstakingly sort through SMF records line by line within a spreadsheet. It’s not efficient, and it’s definitely not helping to achieve IT performance goals.
- Specific data: Traditional SCRT lacks the level of detail many mainframe managers require. If your team is looking to update an application, good luck finding mainframe data specific enough to get the job done. Given the large breadth of information, finding actionable insight is like trying to find a needle in a haystack.
- Up-to-date data: The traditional way of collecting mainframe data is to manually compile SMF records and then export them into a spreadsheet. This is as time consuming as it is inefficient. By the time anyone is able to review the spreadsheet contents, the data is likely obsolete. Without real-time data insight, IT teams are left optimizing according to outdated information.
While logs analysis should be fundamental, checking the above criteria is nearly impossible by default. That’s why zCost has developed the answer to data collection inefficiencies: ZETALY. This software is the premiere logs collection and analytics platform changing the way IT teams optimize system performance and affordability.
Simplify Logs Analysis With ZETALY
ZETALY automatically collects logs in near-real time and displays their contents in an easy-to-use, central platform. All logs in one platform — ZETALY is revolutionizing mainframe analytics by making it easy for anyone on the team to collect, analyze, and actualize data.
This software simplifies data collection by implementing three major process improvements:
- Only collect the logs you need: ZETALY offers revolutionary personalization options, which allow users to collect data catered specifically to their business needs. Simply select the logs you need within the application, and ZETALY will automatically collect all relevant mainframe information.
- Collection that doesn’t exhaust mainframe resources: ZETALY offsets collection activity onto an open platform, so the application can collect event logs while spending minimal (0.2%) mainframe resources.
- Continuous collection: Event logs are captured continuously and in near-real time. The intuitive ZETALY parameters allow the software to collect a huge amount of mainframe data almost as soon as it is created.
Collecting mainframe data has never been this easy. Based on pre-established parameters, ZETALY automatically collects both SYS.MAN files or SMF log stream, so your team can pull from both SMF repositories and store all that information within the platform-agnostic software. Through automated logs collection, not only is z/OS relieved of collection activities, but ZETALY also ensures all mainframe data is relevant, specific, and up-to-date.
Curious what you do with all this data after it’s collected? Check out our ZETALY product sheet, and learn how to take data analytics to the next level. In this guide, you will learn how this ZETALY component helps contextualize analytics and uncover all the KPIs mainframe managers need to improve the performance and affordability of their mainframes.