Operations | Monitoring | ITSM | DevOps | Cloud

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.