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?
Serving over 44% of the world’s websites, Apache is by far the most popular web server used today. Apache, aka Apache HTTP Server, aka Apache HTTPd, owes its popularity to its ease of use and open-source nature but also its inherent flexibility that allows engineers to extend Apache’s core functionality to suit specific needs.
Apache Flume helps organizations stream large log files from various sources to distributed data storage like Hadoop HDFS. This article focuses on the features and capabilities of Apache Flume and how it can help applications efficiently process data for reporting and analytical purposes.
Even though the public cloud has become a commonplace concept in today’s tech world, many companies in a variety of industries remain slow to leverage its advantages. One commonly cited reason for this is that even for experienced IT experts, the transition to and adoption of cloud technologies can be daunting.
Load balancing, traffic management, authentication and authorization, service discovery — these are just some of the interactions taking place between microservices. Collectively called a “service mesh”, these interconnections can become an operations headache when handling large‑scale, complex applications. Istio seeks to reduce this complexity by providing engineers with an easy way to manage a service mesh.
Monitoring is an essential aspect of any IT system. System metrics such as CPU, RAM, disk usage, and network throughput are the basic building blocks of a monitoring setup. Nowadays, they are often supplemented by higher-level metrics that measure the performance of the application (or microservice) itself as seen by its users (human beings on the internet or other microservices in the same or different clusters).
As discussed in our blog post [What is User Activity Monitoring?], user activity monitoring (UAM) is a form of surveillance that provides visibility and insight into employee productivity and engagement while also revealing insider security threats. While UAM on company-owned or company-sanctioned devices and networks is legal, ethical and HR considerations require that UAM be implemented with a high level of professionalism and sensitivity.
