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End User Monitoring 101: Tools - Part 1

Larry Haig

After establishing that End User Monitoring and Front End Optimization (FEO) are alive and well, I thought it would make sense to look at the current landscape of tools that can help in this area.

Application Performance Management (APM) tooling certainly has its place here, particularly for integrated, ongoing monitoring. However, it is probably useful to think of FEO as an extension activity, undertaken separately to the core KPI tracking and issue resolution supported by APM. I will reference APM tooling in the context of the various categories considered. I have split the tooling consideration into two posts: introduction & synthetic testing, and RUM (including mobile).

Let's start with a summary of available tool types (split into two parts), and then a structured FEO process. I am assuming an operations (rather than developer) centric approach. Certainly, the most robust approach to ensuring client side performance efficiency is to bake it in from inception, using established "Performance by Design" principles and cutting edge techniques. However, as in most cases "I wouldn't have started from here" is not exactly a productive recommendation, let's set the scene for approaches to understanding and optimizing the performance of existing web applications.

So, tooling. Any insights gained will start with the tools used. The choice will depend upon the technical characteristics of the target (e.g. traditional HTTP Website, Single Page Application, WebApp, Native Mobile App), and the primary objective of the test phase [the spectrum of (ongoing) Monitoring through to (point) Analysis].

The first hurdle is gaining appropriate visibility. However, it must be noted that any tool will produce data, the key is effective interpretation of the results. This is largely a function of knowledge and control of the test conditions.

So, what are the relevant categories of front end test tooling? The following does not seek to provide a blow-by-blow comparison of the multiplicity of competitors in each category – and in any case, the best choice for you will be determined by your own specific circumstances. Rather, it is a high level category guide. As a general rule of thumb, examples of each category will ideally be used to provide a broad insight into end user performance status and Front End Optimization. Modern APM tools increasingly tick many of these boxes, although some of the more arcane (but useful) details are yet to appear.

As we will see when considering process, FEO practice in Operations essentially consists of two aspects. One is understanding the outturn performance to external end points (usually end users). This is achieved through monitoring, that is, obtaining an objective understanding of transaction, page, or page component response from replicate tests in known conditions, or of site visitors over time.

Monitoring provides information relative to patterns of response of the target site or application, both absolute and relative to key competitors or other comparators.

The other aspect is Analysis of the various components delivered to the end user device. These components fall into three categories: static, dynamic, or logic (JavaScript code). Data for detailed analysis may be obtained as a by-product of monitoring, or from single or multiple point "snapshot" tests. Component analysis will be covered in a subsequent post.

Read End User Monitoring 101: Tools Part 2

Larry Haig is Senior Consultant at Intechnica.

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End User Monitoring 101: Tools - Part 1

Larry Haig

After establishing that End User Monitoring and Front End Optimization (FEO) are alive and well, I thought it would make sense to look at the current landscape of tools that can help in this area.

Application Performance Management (APM) tooling certainly has its place here, particularly for integrated, ongoing monitoring. However, it is probably useful to think of FEO as an extension activity, undertaken separately to the core KPI tracking and issue resolution supported by APM. I will reference APM tooling in the context of the various categories considered. I have split the tooling consideration into two posts: introduction & synthetic testing, and RUM (including mobile).

Let's start with a summary of available tool types (split into two parts), and then a structured FEO process. I am assuming an operations (rather than developer) centric approach. Certainly, the most robust approach to ensuring client side performance efficiency is to bake it in from inception, using established "Performance by Design" principles and cutting edge techniques. However, as in most cases "I wouldn't have started from here" is not exactly a productive recommendation, let's set the scene for approaches to understanding and optimizing the performance of existing web applications.

So, tooling. Any insights gained will start with the tools used. The choice will depend upon the technical characteristics of the target (e.g. traditional HTTP Website, Single Page Application, WebApp, Native Mobile App), and the primary objective of the test phase [the spectrum of (ongoing) Monitoring through to (point) Analysis].

The first hurdle is gaining appropriate visibility. However, it must be noted that any tool will produce data, the key is effective interpretation of the results. This is largely a function of knowledge and control of the test conditions.

So, what are the relevant categories of front end test tooling? The following does not seek to provide a blow-by-blow comparison of the multiplicity of competitors in each category – and in any case, the best choice for you will be determined by your own specific circumstances. Rather, it is a high level category guide. As a general rule of thumb, examples of each category will ideally be used to provide a broad insight into end user performance status and Front End Optimization. Modern APM tools increasingly tick many of these boxes, although some of the more arcane (but useful) details are yet to appear.

As we will see when considering process, FEO practice in Operations essentially consists of two aspects. One is understanding the outturn performance to external end points (usually end users). This is achieved through monitoring, that is, obtaining an objective understanding of transaction, page, or page component response from replicate tests in known conditions, or of site visitors over time.

Monitoring provides information relative to patterns of response of the target site or application, both absolute and relative to key competitors or other comparators.

The other aspect is Analysis of the various components delivered to the end user device. These components fall into three categories: static, dynamic, or logic (JavaScript code). Data for detailed analysis may be obtained as a by-product of monitoring, or from single or multiple point "snapshot" tests. Component analysis will be covered in a subsequent post.

Read End User Monitoring 101: Tools Part 2

Larry Haig is Senior Consultant at Intechnica.

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...