Operations | Monitoring | ITSM | DevOps | Cloud

A log line arrives as plain text and leaves as a record you can query. Six steps sit between those two states. Each one adds something useful, and each one costs you time, CPU, or storage. Most teams never look at that chain until a search comes back empty. Here is what log processing does to an event, step by step: By the end you can look at your own chain. You will know what each step buys you. Six steps turn a raw log line into a searchable record.

Sensitive values reach your logs without anyone deciding they should. A debug line prints a whole request object. An error message carries the query string. A customer email address is suddenly stored in three systems. PII redaction in logs then gets treated as one setting to switch on. In practice it covers four separate treatments. The value is already inside the message before log ingestion finishes. In this blog, you will: By the end you can write a rule for each field and defend it.

In a single morning, a coding agent can pull an open-source model, connect to an unvetted MCP server, and execute a code-optimizing skill from the web. In the rush toward agentic automation, these AI assets quietly bypass traditional security reviews, creating new attack vectors across the software supply chain. Closing this blind spot has been the driving force behind the JFrog AI Catalog since its launch at swampUP 2025.

Learn how NaaS helps MSPs add connectivity services and respond faster to customers without building a global network. For many Managed Service Providers (MSPs), connectivity is still the part of a customer solution they control the least. An MSP may manage the cloud environment and secure access to it, but adding a circuit can still mean carrier lead times and manual coordination. Options may also narrow when the customer enters a new market. This reality doesn’t match with customer expectations.

Datadog’s 2026 State of AI Engineering report showed organizations’ LLM inputs swelling rapidly as context engineering expands. In March 2026, 69% of all input tokens in Datadog customer traces were for system prompts: internal instructions, policy definitions, and tool guidance providing context and guardrails around the user input. This suggests that most context engineering spend among Datadog customers is going toward optimizing repeating system prompts in heavily scaffolded agent systems.

TL;DR Katja Rantala thought her dream job involved international relations and diplomacy. Turns out leading people and companies through change is what she was meant to do. “Some days everything is on fire and it's a catastrophe. The important question is whether the big picture moved.” That is Katja Rantala's description of what it is like to work in an environment that keeps changing shape.

It's been one year since we issued an AI mandate inside Honeycomb, and we've been doing a lot of reflecting internally on how far we've come and how far we have yet to go.

Summary This guide walks through an end-to-end Grafana and InfluxDB 3 integration using a realistic dataset you generate yourself. The tutorial covers getting data in, transforming it, connecting Grafana, and building real dashboards. Table of Contents InfluxDB and Grafana are the most common pairing in time series monitoring, and division of labor between them is simple.

We put a blunt question to developers on a recent live webinar: right now, could you actually prove AI agents are paying off for you or your team? Only 24% said yes. The other 76% were guessing, unsure, or already suspicious that agents are costing more than they’re saving. That gap between adoption and proof is the real story in agentic development right now. Teams aren’t behind on running agents. They’re behind on knowing whether it’s working.

If your team works with SAP HANA, the main challenge usually isn’t finding another dashboard. The real difficulty is identifying which tool can quickly help you trace vague complaints about slow transactions to their root cause. In many setups, SAP HANA monitoring is divided among native SAP interfaces, cloud monitoring, infrastructure dashboards, and the broader monitoring systems used by the rest of IT. This fragmented approach can slow down root cause analysis.