Visualizing Your Log Data

Haim Koschitzky
Learn more about XpoLog

How do we organize log data in a meaningful way that will not only make sense, but also be practical, usable, visible, and accessible quickly; in addition to being organized to support DevOps and APM insights?

Despite numerous log data analysis deployments, we still identify many challenges users face regarding IT log data visualization, analysis, and insights. How can we make sure anomaly detection is fast and easy so that log management does not become too time-consuming? Here are some guidelines for building meaningful operational views and dashboards for IT, leveraging log search, log analysis, machine learning, and advanced analytics.

First Ask Questions

Although stating the obvious, before investing expensive efforts and resources into analyzing data, it is crucial to define your expectations and requirements. While in the past, merely collecting all log data and making it available for search was good enough, this is no longer the case.

In order to ask the right questions, determine what the most important use cases your log data has shown you and what role you want your log data to play in your future ongoing work. To do this, you must monitor system availability, software quality, continuous deployment, application performance, and business insights, troubleshoot, analyze security incidents, compliance audit etc.

There are specific use cases for the application life cycle. Architect, developer, tester, DevOps, APM, operations, and production support all have specific uses cases and requirements. Giving the right answer to the right question makes a big impact and will drive smart actions.

Then Visualize

Once the requirements and expectations are well defined, it is crucial to be able to visualize your findings for further analysis; the more detailed, the better. We recommend creating an App that contains a collection of dashboards. If possible, create a dashboard per topic or use case, and provide each one with a meaningful name (“performance”, “errors”, “user audit”).

Now create search queries, or use out of the box gadgets for analytics, to find example Apps that you will be able to use as examples of best use cases for log analysis data visualization.

How to Visualize

Once you’ve created search queries to analyze data and generate proper result sets, you will need to select the visualization gadget that best reads these result sets and visualizes it in the most effective way.

Here is a result set that aggregated and computed the avg. memory consumption and total memory usage of two application servers. Take a look at the figure below. On gadget 1 you can see the totals over 24 hr aggregated memory consumption at 1 hr intervals. This gadget tells the story of both servers. Gadgets 2 and 3 represent the same data but for each of the individual servers. Once we split the data for each server we discover that each of the servers had a very different memory consumption pattern.

An hourly aggregation for memory is far from being accurate; memory changes at a much faster rate. On the upper row of gadgets we see the totals for both servers (gadget 4), and two additional gadgets, 5 and 6, representing each server in 1 min intervals.


We were looking to monitor our application server memory consumption to avoid spikes that might crash one of our clusters. Choosing the right visualization tools, and in this case, intervals, makes a big difference.

Optimize Insights

Optimize your dashboards and visualization gadgets by verifying that they deliver the insights you’re after in the right resolution. In the example above, analyzing memory for the entire cluster did not provide a clear status image of the memory consumption, but grouping by server and later reducing the time interval resolution to minutes gave a clear understanding of which cluster spiked.

Actions

Once your Apps and Dashboards provide clear views and visualization, it becomes much easier to identify problems, trends, and insights on your IT and applications. Now you can monitor or view the dashboards live. Leverage the visibility and you will be able to take actions that will make your applications more agile, secure, and optimized for the business.

Ask More Questions

Go back to the first step. This is an ongoing process. Data changes every day. The content of logs and other data types is being updated by IT, developers, and vendors continuously. In order to stay ahead, keep asking questions and never stop looking for the answers.

Haim Koschitzky is CEO of XpoLog Ltd.

APM

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Visualizing Your Log Data

Haim Koschitzky
Learn more about XpoLog

How do we organize log data in a meaningful way that will not only make sense, but also be practical, usable, visible, and accessible quickly; in addition to being organized to support DevOps and APM insights?

Despite numerous log data analysis deployments, we still identify many challenges users face regarding IT log data visualization, analysis, and insights. How can we make sure anomaly detection is fast and easy so that log management does not become too time-consuming? Here are some guidelines for building meaningful operational views and dashboards for IT, leveraging log search, log analysis, machine learning, and advanced analytics.

First Ask Questions

Although stating the obvious, before investing expensive efforts and resources into analyzing data, it is crucial to define your expectations and requirements. While in the past, merely collecting all log data and making it available for search was good enough, this is no longer the case.

In order to ask the right questions, determine what the most important use cases your log data has shown you and what role you want your log data to play in your future ongoing work. To do this, you must monitor system availability, software quality, continuous deployment, application performance, and business insights, troubleshoot, analyze security incidents, compliance audit etc.

There are specific use cases for the application life cycle. Architect, developer, tester, DevOps, APM, operations, and production support all have specific uses cases and requirements. Giving the right answer to the right question makes a big impact and will drive smart actions.

Then Visualize

Once the requirements and expectations are well defined, it is crucial to be able to visualize your findings for further analysis; the more detailed, the better. We recommend creating an App that contains a collection of dashboards. If possible, create a dashboard per topic or use case, and provide each one with a meaningful name (“performance”, “errors”, “user audit”).

Now create search queries, or use out of the box gadgets for analytics, to find example Apps that you will be able to use as examples of best use cases for log analysis data visualization.

How to Visualize

Once you’ve created search queries to analyze data and generate proper result sets, you will need to select the visualization gadget that best reads these result sets and visualizes it in the most effective way.

Here is a result set that aggregated and computed the avg. memory consumption and total memory usage of two application servers. Take a look at the figure below. On gadget 1 you can see the totals over 24 hr aggregated memory consumption at 1 hr intervals. This gadget tells the story of both servers. Gadgets 2 and 3 represent the same data but for each of the individual servers. Once we split the data for each server we discover that each of the servers had a very different memory consumption pattern.

An hourly aggregation for memory is far from being accurate; memory changes at a much faster rate. On the upper row of gadgets we see the totals for both servers (gadget 4), and two additional gadgets, 5 and 6, representing each server in 1 min intervals.


We were looking to monitor our application server memory consumption to avoid spikes that might crash one of our clusters. Choosing the right visualization tools, and in this case, intervals, makes a big difference.

Optimize Insights

Optimize your dashboards and visualization gadgets by verifying that they deliver the insights you’re after in the right resolution. In the example above, analyzing memory for the entire cluster did not provide a clear status image of the memory consumption, but grouping by server and later reducing the time interval resolution to minutes gave a clear understanding of which cluster spiked.

Actions

Once your Apps and Dashboards provide clear views and visualization, it becomes much easier to identify problems, trends, and insights on your IT and applications. Now you can monitor or view the dashboards live. Leverage the visibility and you will be able to take actions that will make your applications more agile, secure, and optimized for the business.

Ask More Questions

Go back to the first step. This is an ongoing process. Data changes every day. The content of logs and other data types is being updated by IT, developers, and vendors continuously. In order to stay ahead, keep asking questions and never stop looking for the answers.

Haim Koschitzky is CEO of XpoLog Ltd.

APM

Related Links

Hot Topics

Related Vendors

The Latest

Challenges of DIY Network Automation

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...

Best Practices for Enterprises to Reduce Cloud Costs

Cloud computing has transformed how we build and scale software, but it has also quietly introduced one of the most persistent challenges in modern IT: cost visibility and control ... So why, after more than a decade of cloud adoption, are cloud costs still spiraling out of control? The answer lies not in tooling but in culture ...

CEOs Double Down on AI While Navigating Enterprise Hurdles

CEOs are committed to advancing AI solutions across their organization even as they face challenges from accelerating technology adoption, according to the IBM CEO Study. The survey revealed that executive respondents expect the growth rate of AI investments to more than double in the next two years, and 61% confirm they are actively adopting AI agents today and preparing to implement them at scale ...

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6 Signals That an Architectural Shift Is Underway Across Enterprise Networks

A major architectural shift is underway across enterprise networks, according to a new global study from Cisco. As AI assistants, agents, and data-driven workloads reshape how work gets done, they're creating faster, more dynamic, more latency-sensitive, and more complex network traffic. Combined with the ubiquity of connected devices, 24/7 uptime demands, and intensifying security threats, these shifts are driving infrastructure to adapt and evolve ...

Cisco

Is Better Release Management the Solution to the Persistent Banking App Downtime?

The development of banking apps was supposed to provide users with convenience, control and piece of mind. However, for thousands of Halifax customers recently, a major mobile outage caused the exact opposite, leaving customers unable to check balances, or pay bills, sparking widespread frustration. This wasn't an isolated incident ... So why are these failures still happening? ...

Understanding Zero-Day Vulnerabilities: Why They Matter and How to Stay Protected

Cyber threats are growing more sophisticated every day, and at their forefront are zero-day vulnerabilities. These elusive security gaps are exploited before a fix becomes available, making them among the most dangerous threats in today's digital landscape ... This guide will explore what these vulnerabilities are, how they work, why they pose such a significant threat, and how modern organizations can stay protected ...

Data Center Outages Becoming Less Frequent

The prevention of data center outages continues to be a strategic priority for data center owners and operators. Infrastructure equipment has improved, but the complexity of modern architectures and evolving external threats presents new risks that operators must actively manage, according to the Data Center Outage Analysis 2025 from Uptime Institute ...

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As observability engineers, we navigate a sea of telemetry daily. We instrument our applications, configure collectors, and build dashboards, all in pursuit of understanding our complex distributed systems. Yet, amidst this flood of data, a critical question often remains unspoken, or at best, answered by gut feeling: "Is our telemetry actually good?" ... We're inviting you to participate in shaping a foundational element for better observability: the Instrumentation Score ...

Automating While Keeping Humans in the Loop

We're inching ever closer toward a long-held goal: technology infrastructure that is so automated that it can protect itself. But as IT leaders aggressively employ automation across our enterprises, we need to continuously reassess what AI is ready to manage autonomously and what can not yet be trusted to algorithms ...

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