Managing telemetry data effectively is a serious challenge for today’s engineering teams. In our webinar, Telemetry Data Management: Tales from the Trenches, experts from Mezmo and DZone shared practical strategies for building robust telemetry pipelines that both streamline operations and turn raw data into a strategic asset.

When we talk about Real User Monitoring (RUM), it’s easy to get wrapped up in metrics—the hard numbers that tell us about our users’ experiences. But RUM is more than just data; it’s the foundation for improving performance, an essential key to user experience. The big question is: how do you accurately measure that experience across different kinds of applications?

We’ve improved the filter pane to include: Additionally, a new time-picker option lets you mix absolute and relative times and manually set the date and time to the second. Additionally, you can view your data in either UTC or your local time zone. Saved searches from Explore can now be used to create visualizations and dashboards in OpenSearch Dashboards, streamlining data analysis.

Internet of Things (IoT) devices are everywhere you look. From the smartwatch on your wrist to the security cameras protecting your offices, connected IoT devices transmit all kinds of data. However, these compact devices are different from the other technologies your organization uses. Unlike traditional devices, IoT devices lack a standardized set of security capabilities, making them easier for attackers to exploit.

Cribl employees are renowned for their insatiable curiosity, especially when it comes to their passions. Having been a technical writer for most of my adult life, this goat is deeply passionate about two things: writing engaging content and understanding the mindset of our users. As one of our founders always says, “Software is a people business.” To make my users successful, I need to know how they think. But what if the “user” is a machine? This goat is intrigued.

Database monitoring is crucial for numerous reasons, an example being that monitoring database performance metrics such as query execution times, throughput, and resource utilization helps highlight performance bottlenecks. By conducting this, administrators can enhance database configurations, queries, and indexing by examining these metrics to optimize overall performance.

Sumo Logic Mo Copilot is a natural language assistant that helps first responders derive insights from logs and resolve issues faster using contextual suggestions and plain English queries. It has been in preview since May 2024 with dozens of customers. Choosing a foundation model was a critical step in its development. Let’s explore our high-level requirements for Copilot, the role of foundation models and the rationale for standardizing on Amazon Bedrock.

Today’s IT and security environments have gone from “big” to “massive” in just a decade or two—endpoints have practically exploded (think hundreds of thousands of servers, not just a hundred). Add in a dizzying array of data types and vendors, and what do you get? A whole lot of chaos. So why, oh why, does agent management still feel like it’s stuck in the early 2000s?

Ensuring scalability, performance, and cost-effectiveness is a constant challenge for cloud-native log management and observability. At Sumo Logic, we faced this challenge head-on by transitioning from a stateful, Lucene-based architecture to a completely stateless, Parquet-based architecture. This transformation lets us improve data storage efficiency, streamline operational complexity, and meet the demands of an ever-increasing data scale.

Logz.io introduces its AI Agent in Beta, using GenAI to revolutionize observability. The AI Agent simplifies monitoring with automated data analysis and root cause detection, accelerating issue resolution by 3-5x for beta users—marking a critical step toward fully autonomous observability.