Operations | Monitoring | ITSM | DevOps | Cloud

DNS monitoring asks your name servers the same question on a timer and checks the answer that comes back. Most teams aim it at their public domain and stop there. That covers half the problem. Your public domain and your internal resolver fail in different ways. A check from outside the building never sees the second one. In this blog, you'll see: By the end you'll know which half of your DNS nobody is watching. A DNS check sends a query and reads the reply. The reply can fail three ways.

How often does a lagging replica or a bad network link reach users before your monitoring flags it? The problem grows when every layer has its own tool. Warning signs then show up in different places, often too late to act on. Motadata 8.2.9 came out on October 5, 2026, with eight updates to Motadata ObserveOps. Four of them focus on the network. The other four deal with databases, data streaming, alerting and SEBI compliance reporting. A few add support for technologies Motadata did not cover before.

Summary When production incidents strike, distributed systems can mask issues under a collection of fragmented logs. But all your services share a universal metric: time. That’s good news, because root cause analysis (RCA) is inherently a time series problem. InfluxDB is your central telemetry hub, allowing you to reconstruct operational failures using a reliable, four-step workflow: Table of Contents It’s 2:00 AM, and your monitoring system fires a critical alert.

Your connection is fine in the morning and crawls every afternoon. Or video calls stutter while everything else loads normally. Either pattern could be ISP throttling. It could also be network congestion, or a problem inside your own network. ISP throttling is when your internet service provider intentionally slows your connection based on how much data you use, the type of traffic you send, the time of day, or the plan you’re on. Knowing throttling exists is the easy part. Proving it is harder.

Sentry’s Flutter SDK 10.0 makes streaming traces the default and changes how unhandled Flutter errors affect Release Health. Errors that leave your app running no longer mark the session as crashed, so your crash-free rate better reflects actual app crashes. The release also gives app startup its own trace on Android and iOS, simplifies logs and metrics setup, expands Dio network error capture, and respects Flutter’s sensitive-content markings in Replay and screenshots.

You’ve got 10 minutes before your next meeting when your phone starts ringing. You’re being paged because the frontend is erroring with failed API requests. You open one tool to see what fired, then another to figure out what’s going on. You message a teammate in Slack to see if they’re noticing the same thing. That’s a lot of context switching, and it’s still how a lot of teams' incident response works today. Opening another tool and catching someone up takes time.

Logging as a Service moves your log management to a provider. They collect, parse, store and search your logs for a monthly fee. The fee is where it goes wrong. Providers meter five different things, and the one written into your contract decides whether ordinary growth doubles your bill. In this blog, you'll see: By the end you'll know which meter your workload lands on. Logging as a Service is a cloud-delivered model for collecting and searching logs.