11/15/2021 | Press release | Distributed by Public on 11/15/2021 11:59
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.
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.
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.
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 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.
Log monitoring and analytics help teams to maintain situational awareness in cloud-native environments. Here are some benefits these practices provide:
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:
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.
While log monitoring and analysis are an essential part of IT operations, practicing them effectively in cloud-native environments has some challenges.
For example:
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.
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."