Uncovering the Unseen: How AI is Revolutionizing Observability

Logs: The Key to Resolving Network Incidents

In today’s IT landscape, the amount of data being generated is overwhelming. Organizations are struggling to detect and diagnose issues in real-time, optimize performance, ensure security and compliance, all while working within limited budgets.

Various tools in the modern observability landscape aim to solve this problem by providing DevOps teams and Site Reliability Engineers (SREs) with the ability to analyze logs, metrics, and traces to uncover patterns and understand network incidents. However, the sheer volume of data generated, such as 30 to 50 gigabytes of logs from a Kubernetes cluster daily, can make it difficult for human eyes to catch every suspicious behavior.

Ken Exner, the chief product officer at Elastic, emphasizes the importance of leveraging AI to handle the task of pattern matching in infrastructure monitoring. He mentions that machines are more efficient than humans at this task.

One significant challenge faced by engineers is the overwhelming amount of unstructured data in logs. Traditionally, logs have been used as a tool of last resort due to their complexity. This has led to teams making costly tradeoffs in terms of building data pipelines, dropping valuable log data, or simply ignoring logs altogether.

Elastic, known as the Search AI Company, has introduced a new feature called Streams in observability. Streams aims to transform noisy logs into meaningful patterns and context, making it easier for SREs to identify critical errors and anomalies. By using AI to parse raw logs and extract relevant fields, Streams streamlines the process of log analysis and enables faster issue resolution.

The Evolution of Observability

Streams revolutionizes the traditional observability process that often involves setting up metrics, logs, traces, alerts, and service level objectives. When an alert is triggered, SREs typically navigate through various tools to identify the root cause of the issue, which can be time-consuming.

With AI-powered Streams, logs are not only used reactively but also proactively to anticipate and resolve issues before they escalate. By automating the workflow and providing information-rich alerts, Streams empowers teams to focus on proactive issue resolution.

Looking ahead, large language models (LLMs) are expected to play a significant role in observability. LLMs excel at recognizing patterns in vast datasets, making them ideal for processing log and telemetry data. Automation tooling combined with LLMs could lead to automated runbooks and playbooks that streamline issue resolution.

Addressing Talent Shortages

Embracing AI for observability not only enhances issue resolution but also addresses the talent shortage in IT infrastructure management. By leveraging LLMs, organizations can augment the skills of their teams and empower novice practitioners to act as experts.

Overall, the future of observability lies in harnessing the power of AI to streamline issue resolution, automate remediation steps, and bridge the gap in skill shortages within IT teams. Elastic’s Streams in Observability offers a glimpse into this future, providing a proactive and efficient approach to managing network incidents.

Streams in Elastic Observability is now available. Learn more about Streams by reading the official blog post.

This article is sponsored content produced by Elastic. For more information on sponsored content, please contact sales@venturebeat.com.