Operations | Monitoring | ITSM | DevOps | Cloud

Modern distributed applications are composed of potentially hundreds of disparate services, all containing code from different internal development teams as well as from third-party libraries and frameworks with limited external visibility. Instrumenting your code is essential for ensuring the operational excellence of all these different services. However, keeping your instrumentation up to date can be challenging when new issues arise outside the scope of your existing logs.

ITSM, which stands for IT service management, is a strategy for delivering IT services and support to an organization, its employees, customers and business partners. ITSM focuses on understanding end users’ expectations and improving the quality of both IT services and their delivery. In the early days of computers, employees relied on the company IT department for help whenever a computer issue arose.

In in order to achieve DevOps success, you must measure how well your DevOps initiatives work. Tracking the right DevOps metrics will help you evaluate the effectiveness of your DevOps practices. In this article, I’ll explain many DevOps metrics, including their significance, the key metrics for various goals, and — best of all — tips for improving the score of each DevOps metric discussed here.

If there’s anything I’ve learned, monitoring data is the lifeblood of the business and a superpower for any IT practitioner. Monitoring allows organizations to react to changes, identify and recover, and understand the true health of the business.

Welcome to the second chapter of the handbook on Anomaly Detection for Time Series Data! This series of blog posts aims to provide an in-depth look into the fundamentals of anomaly detection and root cause analysis. It will also address the challenges posed by the time-series characteristics of the data and demystify technical jargon by breaking it down into easily understandable language. This blog post (Chapter 2) is focused on different types of anomalies.

Industrial IoT (IIoT) machines and sensors generate valuable time series data. It’s impossible to derive the insights necessary to inform decisions as a company to produce or operate more efficiently without sending operational technology (OT) data to informational technology (IT) systems.

As APIs play a crucial role in connecting modern cloud applications, monitoring their availability and performance is a must if you want to provide a top-notch experience. A good API monitoring tool will help you build reliable APIs by identifying and resolving the issues before they reach your users. If you’re interested in such a solution, look no further. In this article, we reviewed some of the best API monitoring tools and services available today, both open source and commercial.

Solr is widely adopted by startups and enterprises alike. It’s powerful and open-source, so it’s very appealing to just about everyone looking for a search platform to build off of. Being easily accessible, many people overlook the importance of monitoring Solr. Even when that importance is put into question, a lot of people continue with the trend and use an open-source tool for their monitoring needs.

The World Wide Web’s transmission system is built on HTTP. To ensure an application that uses the HTTP transmission works, you must monitor it constantly. This is where an HTTP monitor comes in. In this tutorial, we’ll cover the fundamentals of HTTP monitors, including what they are, why they matter, and how to set one up.

Following its release under the open-source Apache 2.0 license in 2010, Elasticsearch rose to prominence as the world’s most popular enterprise search engine. Elasticsearch is frequently deployed alongside Logstash and Kibana, a combination known as the ELK stack, to enable log analytics use cases that include application observability, security log analysis, and understanding user behavior.