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The latest News and Information on Observabilty for complex systems and related technologies.
Moving Beyond SolarWinds: A Guide to Modern Observability
Industry-leading observability experts provide strategic guidance on why and how modern IT teams are successfully moving beyond SolarWinds to more resilient, cloud-native platforms. IT teams running SolarWinds often know the pain points well before they start evaluating alternatives: separate modules for different monitoring needs, a self-hosted deployment model that requires ongoing maintenance, and pricing that gets harder to predict after each acquisition.
How Scalability Works in SolarWinds Observability Self-Hosted
Cheryl Nomanson, SolarWinds staff technical trainer, provides a comprehensive overview of SolarWinds architecture and scaling options for self-hosted deployments. She explains the centralized deployment model starting with a single SolarWinds server that handles polling, web console, and database connections. The presentation covers key scaling indicators including polling thresholds that warn users at 85% capacity and alert at 100%. She demonstrates how to add up to 100 polling engines per server and additional web servers to handle more concurrent users.
Taming Log Noise With the OpenTelemetry Collector's Drain Processor
Do you receive 50 million log lines per day and struggle to see what actually matters? Health checks, heartbeat pings, connection pool messages—they all drown out the errors and anomalies you're trying to find. Most teams deal with this by writing filter rules to drop the noisy patterns. But those rules are manual, per-pattern, and brittle. A new deployment changes a log format and the filter misses it. A new service starts logging a chatty startup sequence nobody thought to exclude.
Get Observability in the Terminal, for You and Your Agents: gcx
The way you write code is changing, which means the way you observe your systems and respond to issues needs to change, too. Engineers today spend much of their day working via command line, as agentic tools like Cursor and Claude Code have become highly effective at handling many day-to-day engineering tasks. This greatly accelerates code generation, but it doesn't solve for the context switching that comes when you have to jump into another tool that's not part of this new, faster workflow.
Why Does MTTD Stay High Despite Observability Tools Running?
Monitoring coverage, anomaly detection, and SLO-based alerting have significantly narrowed detection windows for most failure types, but MTTD remains stubbornly high for a specific silent failure. This blog covers why type mismatches, swallowed exceptions, and values that pass validation without occurring without triggering errors, and what changes when your monitoring stack can generate those signals without waiting for a failure to surface them.
Add dynamically updating context to logs with Reference Tables and Observability Pipelines
Security and platform engineering teams rely on context-rich logs to investigate threats, prioritize incidents, and meet compliance requirements. Context is often stored separately from applications that generate logs, in sources like threat intelligence feeds in Snowflake, asset lists in Amazon S3, ownership data in ServiceNow CMDB, and risk scores produced in Databricks.
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Understanding the Three Pillars of Observability: Logs, Metrics and Traces
Many people wonder what the difference is between monitoring vs. observability. While monitoring is simply watching a system, observability means truly understanding a system's state. DevOps teams leverage observability to debug their applications, or troubleshoot the root cause of system issues. Peak visibility is achieved by analyzing the three pillars of observability: Logs, metrics and traces. Depending on who you ask, some use MELT as the four pillars of essential telemetry data (or metrics, events, logs and traces) but we'll stick with the three core pillars for this piece.
New in the Honeycomb Academy: Learn to Use the Honeycomb MCP
Two things happen when engineers first connect the Honeycomb MCP to their AI assistant. The first is the blank page problem. The Honeycomb UI gives you something to react to: a heatmap, a query builder, a trace to click into. An AI assistant gives you a cursor and nothing else. When you don't know where to start, that's a hard place to be. The second shows up right after you get past the first one. You ask a question, you get a confident-sounding answer, and you're not sure whether to trust it.
Service-Centric Observability as the Control Layer
If distributed architectures have altered how systems degrade, then the way organizations model operational must evolve accordingly. Threshold monitoring evaluates individual metrics. Correlation clusters related alerts. Neither, on its own, explains how instability in one component alters exposure across an interconnected service landscape. In conversations at Nexus Live 2025, ScienceLogic’s annual customer conference, leaders described this distinction with clarity.