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The Place Where Modern Operations & Technology Come Together
The latest News and Information on Log Management, Log Analytics and related technologies.
How to benchmark Elasticsearch performance with ingest pipelines and your own logs
When setting up an Elasticsearch cluster, one of the most common use cases is to ingest and search through logs. This blog post focuses on getting a benchmark that will tell you how well your cluster will handle your workload. It allows you to create a reproducible environment for testing things out. Do you want to change the mapping of something, drop some fields, alter the ingest pipeline?
Deep Temporal Observability - Correlate Metrics with logs & traces
We built deep Temporal observability in SigNoz.
The Best Open-Source Dashboard Tools for 2025: Expert Guide to Choosing the Right One
Table of Contents In today’s digital operations, dashboards aren’t just nice-to-haves—they’re essential. Teams across engineering, product, operations, and business intelligence rely on real-time data visibility to monitor systems, analyze trends, and catch anomalies before they escalate. For many organizations, open-source dashboard tools offer the best combination of flexibility, transparency, and cost-efficiency.
Agentic AI in financial services: The rise of autonomous intelligence
Agentic AI is coming to financial services. Elastic provides the data foundation and tools to make it work. In a recent talk at Stanford University, Jamie Dimon, chairman and CEO of JPMorganChase, addressed the firm’s use of AI and ended with mentioning that agentic AI was the next frontier of AI at the firm, inferring it wasn’t ready to be deployed yet. Let’s break down why that may be the case and what the financial services industry can do to become more comfortable with agentic AI.
Introducing Coralogix Continuous Profiling
Debug faster, improve application performance, and lower your cloud costs - without slowing down production. Traditional profiling solutions come with a heavy price—added latency, excessive resource consumption, and performance degradation. At, we’re changing the game with Continuous Profiling, the first of its kind to offer real-time, kernel-level visibility into application performance without any code changes or production impact.
Meta-monitoring Loki (Loki Community Call May 2025)
In this Loki Community Call, we talk about the need for meta-monitoring Loki: why Loki needs to be monitored, what to watch out for, and how to do it. We talk about different ways to get information from Loki that allow you to make it reliable, consistent, and performant, including a Helm chart to deploy a meta-monitoring stack on Kubernetes. We discuss the Loki mixin for Grafana and how to use it to visualize data about Loki. On the call are Jay Clifford, Nicole van der Hoeven, and Dylan Guedes from Grafana Labs.
Modern Logging, Smarter Pricing: Why Graylog's Consumption Model Just Makes Sense
In the world of log management and security analytics, one thing is abundantly clear: data volumes fluctuate. Yet most pricing models haven’t caught up. Traditional ingest-based licensing models force organizations to size their license needs based on a worst-case capacity scenario—the “high-water mark”—whether those spikes are rare and/or expected.
Splunk Observability Cloud's AI Assistant in Action | Practical Examples | Part 1
In this video, we’ll provide practical, real-time examples demonstrating how to effectively use the AI Assistant in Splunk Observability Cloud. You'll learn how the AI Assistant can quickly identify unknown issues in your environment, perform detailed root cause analysis, analyze service performance and deployment impacts, and even help manage infrastructure costs and compliance. TOC.
Easiest Way to Monitor Loki Performance With Telegraf
Loki is a powerful, scalable log aggregation system designed by Grafana to efficiently collect, store, and query logs. It’s often deployed alongside Prometheus as part of modern observability stacks. Loki’s design emphasizes cost-effective storage by indexing only metadata, which makes it a great choice for high-volume environments. But while Loki excels at log ingestion and indexing, many teams overlook the critical task of monitoring Loki itself.