Imagine an AI assistant that could automatically surface a whole host of useful incident response data points with just a prompt. Well, you won't need to imagine for much longer. That's exactly what we built in Assistant, one of our newest features powered by AI. In this episode, you'll hear from Charlie, the project lead for Assistant, to get a peek behind this game-changing product.

Elastic APM for native apps provides auto-instrumentation of outgoing HTTP requests and view-loads, captures custom events, errors, and crashes, and includes pre-built dashboards for data analysis and troubleshooting purposes Elastic® APM for iOS and Android native apps is generally available in the stack release v8.12. The Elastic iOS and Android APM agents are open-source and have been developed on-top, i.e., as a distribution of the OpenTelemetry Swift and Android SDK/API, respectively.

How often should you ping your site? Should you be checking every few minutes, or every hour? Surely you have other ways to detect problems, so maybe just a daily check of your API and main page would be enough, right? While there’s no single right answer for everyone, this post tries to break down how you can find the right cadence for your site checks.

In this article, we will use OpenTelemetry to instrument a sample Flask app for traces. Flask is one of the most popular web application frameworks of Python. It consists of Werkzeug WSGI toolkit and Jinja2 template engine. Instrumentation is the biggest challenge engineering teams face when starting out with monitoring their application performance. OpenTelemetry is the leading open-source standard that is solving the problem of instrumentation.

Let’s face it. Incidents will always happen. We simply can’t prevent them. But we can strive to mitigate the impact incidents have on our product and customers. Ensuring high reliability depends on quickly and effectively finding and fixing problems. This is where the metric MTTR, standing for “mean time to restore” or “mean time to resolve,” becomes valuable for organizations.

This article was originally published on IIoT World and is reprinted here with permission. In the rapidly evolving world of Industrial Internet of Things (IIoT), organizations face numerous challenges when it comes to managing and analyzing the vast amounts of data generated by their industrial processes. Data generated by instrumented industrial equipment is consistent, predictable, and inherently time-stamped.