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The latest News and Information on Log Management, Log Analytics and related technologies.

Understanding IoT Logging Formats in Azure and AWS

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.

Best MySQL Monitoring Tools

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.

Differentiating Sumo Logic Mo Copilot using Amazon Bedrock

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.

Big Data, Zero Hassle: Cribl Edge for Centralized Agent Management

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?

Introducing the Logz.io AI Agent, Accelerating the Future of Observability

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.

From stateful to stateless: Sumo Logic's transition from Lucene to Parquet-based architecture

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.

Threat Hunting with Cribl Search

Imagine you’re the protector of a castle. Your walls are tall, the gates are strong, and the guards are well-trained. But what if an intruder was still able to slip past your defenses? Even with the best security tools, not every threat will be caught. Threat hunting is the proactive approach to finding attackers that might have bypassed your defenses.

The Path to Autonomous Observability

Autonomous observability for system monitoring and management aims to use GenAI and machine learning to automatically detect, diagnose and resolve issues. In conversations about cloud observability today, discussions often shift from “what’s possible” to “what’s practical.” Too often, these conversations highlight the shortcomings of current observability processes, tools and financial models.

Enhancing Log Analysis with Machine Learning (ML)

Log Analysis has been a beneficial practice for organizations for numerous years, and over these years it has continuously evolved. This has been in part driven by the increasing volume of logs that companies are required to monitor. Now, log analysis is shifting again, incorporating machine learning (ML) and artificial intelligence (AI) to assist data analysts in identifying system log patterns and anomalies.

October '24 BindPlane Update

I'm covering our powerful new feature: the coalesce processor in BindPlane! I’ll walk you through how to use it to simplify your telemetry data by merging mismatched field names—like user and username—into one unified field (usr). We’ll configure a BindPlane Gateway, capture telemetry from various sources, and route it all to Honeycomb and S3. With the coalesce processor, field names get standardized quickly, making your dashboards and alerts far more intuitive.