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.

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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.

Related Links

Hot Topics

Related Vendors

The Latest

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

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 ...

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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 ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

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

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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 ...

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When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...