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Application Performance Monitoring Cheat Sheet

Phee-Lip
BHP

A brief introduction to Applications Performance Monitoring (APM), breaking it down to a few key points:

1. It is different from conventional infrastructure monitoring which primarily captures and reports on hardware performance such as CPU and memory but APM also covers more advanced infra technology, such as containers, etc.

2. APM tells you how the application, which sits on top of the infrastructure, is performing by going deep into the code level and it includes the capability to monitor microservices and different types of programming languages.

3. Recently (actually not so recent) it has expanded to include user experience monitoring which encapsulates capturing of the user journey, reporting of errors and performance of user-triggered activities (click-on-page) as user behavior and experience are becoming more essential.

4. With a huge amount of data being collected, it is only natural that it has become a big data platform for companies to gain insights into their operations and business. Hence the expansion into analytic!

A few important lessons which I have learned over the years:

1. Many organizations are still "stuck" at reporting service availability. This requires a fundamental mindset change as the spotlight is now on application performance and service quality. These are critical aspects of digitization which no companies can afford to neglect.

2. APM can pinpoint the problems but it can't fix them for you. At least not now, perhaps later with AI. It is not a silver bullet and it draws out a very important point that organizations MUST HAVE system/domain expertise to maintain and improve the systems which are the most critical to their business!

3. Not everything is created equally. Hence you don't need a full-fledged APM tool for every system. Focus on the most critical systems. That will not only save you money but enable you to have undivided attention only on those which you care deeply about.

4. It is hard to find the best APM tool in every aspect of its capabilities. You just have to decide what are the most crucial elements for success and find the best solutions for them. You may end up with a couple of tools, hence it will be good to look at how you can gain a cohesive view across these tools to form your master service performance dashboard. Some form of integration may be required.

5. Many organizations have a central monitoring team who have eyes-on monitoring 24x7. This is old school and ineffective. Natural language processing (NLP) is the future with exception-based voice notification and an intelligent contextual query to have a deep understanding of systems health and performance, anytime, anywhere.

APM is a complex topic as it is a multi-faceted discipline. It will continue to evolve, branching into other domains such as service automation (self-healing), service management and deep learning. These areas have been coined as AIOps by Gartner, heavily anchored on AI. Definitely a space to watch out going forward!

Phee-Lip is Principal, APM Practice Lead, at BHP

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Application Performance Monitoring Cheat Sheet

Phee-Lip
BHP

A brief introduction to Applications Performance Monitoring (APM), breaking it down to a few key points:

1. It is different from conventional infrastructure monitoring which primarily captures and reports on hardware performance such as CPU and memory but APM also covers more advanced infra technology, such as containers, etc.

2. APM tells you how the application, which sits on top of the infrastructure, is performing by going deep into the code level and it includes the capability to monitor microservices and different types of programming languages.

3. Recently (actually not so recent) it has expanded to include user experience monitoring which encapsulates capturing of the user journey, reporting of errors and performance of user-triggered activities (click-on-page) as user behavior and experience are becoming more essential.

4. With a huge amount of data being collected, it is only natural that it has become a big data platform for companies to gain insights into their operations and business. Hence the expansion into analytic!

A few important lessons which I have learned over the years:

1. Many organizations are still "stuck" at reporting service availability. This requires a fundamental mindset change as the spotlight is now on application performance and service quality. These are critical aspects of digitization which no companies can afford to neglect.

2. APM can pinpoint the problems but it can't fix them for you. At least not now, perhaps later with AI. It is not a silver bullet and it draws out a very important point that organizations MUST HAVE system/domain expertise to maintain and improve the systems which are the most critical to their business!

3. Not everything is created equally. Hence you don't need a full-fledged APM tool for every system. Focus on the most critical systems. That will not only save you money but enable you to have undivided attention only on those which you care deeply about.

4. It is hard to find the best APM tool in every aspect of its capabilities. You just have to decide what are the most crucial elements for success and find the best solutions for them. You may end up with a couple of tools, hence it will be good to look at how you can gain a cohesive view across these tools to form your master service performance dashboard. Some form of integration may be required.

5. Many organizations have a central monitoring team who have eyes-on monitoring 24x7. This is old school and ineffective. Natural language processing (NLP) is the future with exception-based voice notification and an intelligent contextual query to have a deep understanding of systems health and performance, anytime, anywhere.

APM is a complex topic as it is a multi-faceted discipline. It will continue to evolve, branching into other domains such as service automation (self-healing), service management and deep learning. These areas have been coined as AIOps by Gartner, heavily anchored on AI. Definitely a space to watch out going forward!

Phee-Lip is Principal, APM Practice Lead, at BHP

Hot Topics

The Latest

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

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 ...