Operations | Monitoring | ITSM | DevOps | Cloud

Open source software adoption continues to grow. Tools like Kafka and Solr are widely used in small startups, ones that are using cloud ready tools from the start, but also in large enterprises, where legacy software is getting faster by incorporating new tools. In this second part of our Solr monitoring series (see the first part discussing Solr metrics to monitor), we will explore some of the open source tools available to monitor Solr nodes and clusters.

During the spring and summer of 2018 I was negotiating a possible acquihire of Checkly / moi with Datadog. I'm only writing about it right now because Datadog recently launched their Synthetics product — the product I would be involved with as some form of product manager. This post details how this all came to be, what steps we took in the process and some reflections on the whole thing.

Nowadays, everyone uses WordPress on a regular basis to write articles, make notes and for just about everything. If you are working on multiple WordPress-related websites, a time will definitely come when you will find it difficult to manage everything at the same time. This is where the role of ManageWP or Manage WordPress becomes important.

Spark is known as a fast general-purpose cluster-computing framework for processing big data. In this post, we’re going to cover how Spark works under the hood and the things you need to know to be able to effectively perform distributing machine learning using PySpark. The post assumes basic familiarity with Python and the concepts of machine learning like regression, gradient descent, etc.

At LogicMonitor, we talk to customers and prospects constantly. This enables us to observe trends in requests for new features and functionality. There are two major topics that prospects and customers have been raising recently.

While Skylight was originally developed to instrument web requests, we understand the web interface is only one part of your server-side application. For this reason, we've been hard at work preparing Skylight for Background Jobs!

So you want to A/B test your web app. The idea is simple, but the details can get messy, and you don’t want to re-invent the wheel. Services like Optimizely are pretty good, but they can be expensive and full of features you don’t need immediately. In this post, we’ll share how Sentry wrote an experimentation system with minimal work.

One of the most common questions we get at Honeycomb is about how to control costs while still achieving the level of observability needed to debug, troubleshoot, and understand what is happening in production. Historically, the answer from most vendors has been to aggregate your data–to offer you calculated medians, means, and averages rather than the deep context you gain from having access to the actual events coming from your production environment.