Cracking the Code on APM Big Data
May 13, 2014

Anand Akela

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Everyone agrees that IT is under pressure to deliver fast and reliable application performance and customer experiences that exceed expectations. And, if a customer-facing app goes down, the implications can be quite drastic as we can see in the article "5-minute outage costs Google $545,000 in revenue" or "WhatsApp experiences major outage". The ramifications include lost revenue, profits, and damaged reputation – not to mention resulting shake-ups in responsible IT departments.

The customer-facing applications that we as consumers rely on every single day have grown very complex, running on an infrastructure that consists of multiple tiers and platforms across physical, virtual and hybrid cloud environments. Ensuring the optimal end-user experience for the new Web, cloud, and mobile apps add to an already overburdened IT workload. The manual processes and transactional-monitoring tools of the past can't keep up with the complexity of these new mission-critical applications. They don't give IT staff the support they need to identify and solve problems quickly, and deliver against their service level objectives.

Application Performance Management (APM) software addresses these challenges in the complex, dynamic, and interconnected application environment. However, monitoring complex applications, business transactions and ensuring exceptional end user experience 24x7, generates a big pile of data.

A well-configured APM system points to root causes and enables technical investigations, but even the best configured system may still miss critical warning signs if operators rely only on simple thresholds and baselines. Complex application environments need good, out-of-the-box analysis of all available APM Big Data to complement the special configuration of dashboards, which serve particular purposes. This is how we can crack the code on big data.

Anand Akela is VP of Product Marketing at Tricentis
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