RabbitMQ is an open source message broker that was built to implement AMQP in 2007, and over the past twelve years has grown to include HTTP, STOMP, SMTP and other protocols via an ever growing list of plugins.
Nginx is an extremely popular open-source web server serving millions of applications around the world. Second only to Apache, Nginx’s owes its popularity as a web server (it can also serve as a reverse proxy, HTTP cache and load balancer) to the way it efficiently serves static content and overall performance.
Logs need to be stored. In some cases, for a long period of time. Whether you’re using your own infrastructure or a cloud-based solution, this means that at some stage you’ll be getting a worried email from your CFO or CPO asking you to take a close look at your logging architecture. This, in turn, will push you to limit some data pipelines and maybe even totally shut off others. Maybe we don’t need those debug logs after all, right? Wrong.
The online world is full of contrasts. On the one hand, you have site reliability engineers whose job is to keep the business running by ensuring an app’s smooth operations. On the other hand, you have the DevOps staff, whose goal is to minimize cycle time—the time from business idea to feature in production. These two teams can have conflicting objectives.
Logs have always been a crucial part of applications, providing insight into an application’s every operation and auditing all of its activities. Yet to date, logs have been used primarily for researching incident details or applicative failures. Only recently have R&D and operations teams started paying closer attention to logs in an effort to identify incidents as they occur and recognize trends that can prevent future pitfalls.
Developer teams and even operational teams often ignore monitoring applications. Deadlines, inexperience, company culture, and management can lead to poor or neglected monitoring inside developing platforms. Automating all monitoring tasks is an excellent way to avoid this scenario. Automation leads to lower costs, less time spent solving issues, and more efficient teams.
Is monitoring in the cloud special enough to warrant a list of tips and best practices? We think so. On the one hand, monitoring in the cloud might seem easy since there is a large number of solutions to choose from. On the other hand, though, the dynamic and distributed nature of the cloud can make the process much more challenging. In this article, we’ll cover ten tips and best practices that will help you ace your cloud monitoring game.
As if the temperature this summer was not high enough, this new major release of the Elastic Stack promises turns it up a notch with some hot new features. Bundling new ETL capabilities in Elasticsearch, a bunch of improvements in Kibana and a lot of new integration goodness in Filebeat and Metricbeat, Elastic Stack 7.3 is worth 5 minutes of your time to stay up to date.
Hive and Spark are two very popular and successful products for processing large-scale data sets. In other words, they do big data analytics. This article focuses on describing the history and various features of both products. A comparison of their capabilities will illustrate the various complex data processing problems these two products can address.
In the world of Software Engineering, observability seems to be the talk of the town. We discuss it at conferences, read about it in blogs or articles, and see it promised to us by vendor after vendor. But what is observability? What issues have recently evolved to make it such an integral concept? What strategies are engineers employing to ensure observability? And most importantly of all, why are engineers looking to achieve it?
