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Cracking the Code on APM Big Data

Anand Akela

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.

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

Cracking the Code on APM Big Data

Anand Akela

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.

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...