Why log monitoring and log analytics matter in a hyperscale world

Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures.

In fact, the global log management market is expected to grow from 1.9 billion in 2020 to $4.1 billion by 2026 according to numerous market research reports. Driving this growth is the increasing adoption of hyperscale cloud providers (AWS, Azure, and GCP) and containerized microservices running on Kubernetes. The flexibility of these environments also makes them more complex, and brings an exponential increase in the volume, velocity, and variety of logs. To figure out what’s going on in these increasingly complex environments—and more importantly, to harness their operational and business value—teams need a smarter way to monitor and analyze logs.

Let’s take a closer look at logs, log monitoring, and log analytics to understand what they are and why they are so critical for establishing and maintaining a healthy modern cloud architecture.

What are logs?

A log is a detailed, timestamped record of an event generated by an operating system, computing environment, application, server, or network device. Logs can include data about user inputs, system processes, and hardware states.

Log files contain much of the data that makes a system observable: for example, records of all events that occur throughout the operating system, network devices, pieces of software, or even communication between users and application systems. “Logging” is the practice of generating and storing logs for later analysis.

What is log monitoring?

Log monitoring is a process by which developers and administrators continuously observe logs as they’re being recorded. With the help of log monitoring software, teams can collect information and trigger alerts if something happens that affects system performance and health.

DevOps teams often use a log monitoring solution to ingest application, service, and system logs so they can detect issues at any phase of the software delivery life cycle (SDLC). Whether a situation arises during development, testing, deployment, or in production, it’s important to work with a solution that can detect conditions in real-time so teams can troubleshoot issues before they slow down development or impact customers.

But to determine root causes, logs must be analyzed.

What is log analytics?

Log analytics is the process of evaluating and interpreting log data so teams can quickly detect and resolve issues. For example, an error message or application alert can indicate something went wrong, but it requires investigation into the logs to understand exactly what happened where, when, and with what outcome so teams can take appropriate action. This analysis is useful for routine application performance monitoring, and also for CI/CD feedback loops, optimizing Kubernetes workloads, application security, business analytics and standards compliance to name just a few.

You can think of log analytics as a science that seeks to make sense of system and application-generated information. However, the process of log analysis can become complicated without the proper tools.

Log monitoring vs log analytics

Log monitoring and log analytics are related but different concepts that work in conjunction to ensure the health and optimal operation of applications and core services.

Whereas log monitoring is the process of tracking ingested and recorded logs, log analytics evaluates those logs and their context for the significance of the events they represent. This includes troubleshooting issues with software, services, and applications, and any infrastructure they interact with, such as multicloud platforms, container environments, and data repositories.

Log monitoring and analytics work in conjunction to ensure an application is performing as it should be, and to determine how a system could be improved.

Log analytics also help identify ways to make infrastructure environments more predictable, efficient, and resilient. Together, they provide continuous value to the business.

Benefits of log monitoring and log analytics

Log monitoring and analytics help teams to maintain situational awareness in cloud-native environments. Here are some benefits these practices provide:

  1. Faster incident response and resolution. Log monitoring and analysis helps teams respond to incidents faster and discover issues earlier before they affect end users.
  2. More automation. With clear insight into crucial system metrics, teams can automate more processes and responses with greater precision.
  3. Optimized system performance. Log analysis can reveal potential bottlenecks and inefficient configurations so teams can fine-tune system performance.
  4. Increased collaboration. A single source of high-fidelity log analytics benefits system administrators, cloud architects, and operations teams so they can create more resilient multicloud environments.
  5. Accelerated innovation. An efficient, automated log monitoring and analytics solution can free teams up to focus on innovation that drives better business outcomes.

Use cases for log monitoring and log analytics

Anything connected to a network that generates a log of activity is a candidate for log monitoring and analysis. As solutions have evolved to leverage artificial intelligence, the variety of use cases has extended beyond break-fix scenarios to address a wide range of technology and business concerns. For example:

  • Infrastructure monitoring to automatically track modern cloud infrastructure, including hosts, VMs, PaaS such as AWS, Azure, GCP, container platforms such as Kubernetes, OpenShift, and Cloud Foundry, network devices, process detection and resource utilization, network usage and performance, log monitoring, third-party data and event integration, and so on
  • Applications and microservices performance monitoring to discover dynamic microservices workloads running inside containers, and to detect and pinpoint issues before real users are impacted
  • Digital experience monitoring including real-user monitoring, synthetic monitoring, and mobile app monitoring to ensure every application is available, responsive, fast, and efficient across every channel
  • Application security to automatically detect vulnerabilities across cloud and Kubernetes environments
  • Business analytics for real-time visibility into business KPIs to improve IT and business collaboration
  • Cloud automation/orchestration for DevOps and SRE teams to build better quality software faster by bringing observability, automation and intelligence to DevOps pipelines

In modern environments, turning the crush of incoming logs and data into meaningful use cases can quickly become overwhelming. Let’s look at some challenges behind log monitoring and log analysis and what organizations are doing to overcome these issues.

Challenges to monitoring and log analytics

While log monitoring and analysis are an essential part of IT operations, practicing them effectively in cloud-native environments has some challenges.

For example:

  • Lack of end-to-end observability. Observability means being able to measure an individual system’s current state based on the data it generates. As environments become bigger and more complex, observability across the full technology stack (thousands of interdependent microservices spread across multiple clouds) becomes increasingly difficult.
  • Inadequate context. Logs are often collected in data silos, with no relationships between them and aggregated in meaningless ways. Without meaningful relationships between the silos, you’re often looking for a few traces among billions to know whether two alerts are related or how users may be impacted by them.
  • Guessing at the root cause. Too often, logging tools leave you clicking through data and poring through logs trying to deduce root causes based on simple correlations. Lack of causation makes it difficult to quantify impact to users or determine which optimization efforts are delivering performance improvements.
  • Difficulty understanding the business impacts. Because digital systems underpin every modern organization, log analysis has the potential to unlock critical insights to help with making data-driven business decisions. However, most log analytics tools lack sufficient observability, context, and precision to reveal how applications, services, or development processes are impacting the business.
  • High cost and blind spots. To avoid the high data-ingest costs of traditional log monitoring solutions, many organizations exclude large portions of their logs and perform minimal sampling. Although cold storage and rehydration can mitigate high costs, it is inefficient and creates blind spots.

With the complexity of modern multicloud environments, traditional aggregation and correlation approaches are inadequate to quickly discover faulty code, anomalies, and vulnerabilities. And too often, organizations have implemented multiple disparate tools to address different problems at different phases, which only compounds the complexity.

How Dynatrace unlocks the value of log monitoring and analytics

Logs are an essential part of the three fundamental pillars of observability: metrics, logs, and traces. End-to-end observability is crucial for gaining situational awareness into cloud-native architectures. But logs alone aren’t enough. To attain true observability, organizations need the ability to determine the context of an issue, both upstream and downstream. Equally important is leveraging user experience data to understand what’s affected, what the root cause is, and how it impacts the business.

To overcome these challenges and to get the best intelligence for log monitoring and analytics, organizations need to work with a solution that takes analytics to the next level.

Using a real-time map of the software topology and deterministic AI, Dynatrace helps DevOps teams automatically monitor and analyze all logs in context of their upstream and downstream dependencies. This broad yet granular visibility enables analysts to understand the business context of an issue and automatically pinpoint its precise root cause down to a particular line of code.

Unlike tools that rely on correlation and aggregation, the Dynatrace AIOps platform approach enables teams to speed up and automate incident responses. As a single source of truth, Dynatrace’s combination of precise answers and automation frees up teams to optimize apps and processes, improve system reliability, and drive better business outcomes.

To learn more about how Dynatrace can turn your organization’s logs into critical insights, join us for the on-demand power demo “Observe all your logs in context with Dynatrace log monitoring.”

Stay updated