Gemini Cloud Assist: Proactive cloud operations that work for you, even before you ask
The redesigned Gemini Cloud Assist proactively executes tasks such as designing applications and optimizing costs that used to need human oversight.
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The latest News and Information on Log Management, Log Analytics and related technologies.
Join operator and Query Agent for smarter log analysis
Sumo Logic’s log analytics capabilities have always provided the greatest insights to help you secure, monitor and troubleshoot your environment. Now, with our Query Agent, as part of Dojo AI, creating optimized log searches with natural language is even easier. Query Agent works with a wide variety of operators, including the join operator, for parsing, aggregation, data transformation, filtering, advanced analysis and lookup.
DataPrime at Ingest: Fine-Grained TCO Routing with DPXL
The real economic decision for observability happens at ingest, before storage, billing, and retention choices are locked-in. Until now, the logic governing that decision could only see three broad fields: application, subsystem, and severity. That just changed. TCO routing now matches on any field in the event payload, including nested keys, custom fields, and event body content, using DPXL, the DataPrime Expression Language.
What is Network Monitoring? Why Every IT Team Needs It (2026)
Learn what network monitoring is and why it’s critical for IT teams in 2026. Discover how it works, key metrics to track, and how to prevent downtime before users are impacted. Modern IT environments are complex—network monitoring helps you detect issues early, reduce downtime, and keep your infrastructure running smoothly. Watch now and monitor your network with confidence. Don’t forget to like, share, and subscribe for more IT insights.
Microlesson: Overview of OpenTelemetry Architecture
The video explains OpenTelemetry Collector Architecture; describes how OpenTelemetry works, and how the OTel Collector fits in.
Observability is a design problem: Live Laugh Logs ep. 1 - KubeCon Amsterdam 2026
What happens when 20,000 engineers descend on Amsterdam to talk about Kubernetes and AI? Welcome to Episode 1 of Live Laugh Logs, the podcast from Annie, Lewis and Andre from the Coralogix Developer Relations team where we will get together and recap everything going on in our worlds! We had an amazing time at KubeCon in Amsterdam and had loads of insights from the talks we went to around designing observability systems, all the AI tools being created and how to observe them, and using agent-generated code.
Building Audit-Ready Observability for Digital Banking
Most observability platforms are built to answer one question: what’s broken right now. Regulators are asking a different one: what happened, exactly, and can you prove it? Digital banking operates under constant regulatory scrutiny, where frameworks like DORA, PCI-DSS, and GDPR require every incident to be fully reconstructed across systems, timelines, and access. Systems can recover quickly, but the ability to explain what happened often remains fragmented across tools and teams.
Debug frontend issues with AI: Real user monitoring meets the Coralogix MCP server
It is 2 AM. Someone on-call gets paged. Conversion rates on the checkout page dropped 30 percent in the last hour. The immediate questions are familiar. Is this a JavaScript error? A slow API call? A broken third-party script? A performance regression that never throws an exception but quietly drives users away? In most teams, answering those questions is not hard because the data is missing. It is hard because the investigation is split across too many places.
The End of Manual Instrumentation: Scaling Observability with OTel OBI & Coralogix
Traditionally, achieving deep visibility into distributed systems required significant trade-offs in engineering time. Collecting meaningful application metrics and traces required teams to embed language-specific agents, modify source code, or manage complex library dependencies across every service.
AI Costs Way More Than You Think
Here's why AI companies don't want you to know how much AI actually costs.