In Parts One and Two of this blog, we looked back at the ongoing open source licensing wars, focusing on the evolving situation between Elastic N.V. and AWS. In this final installment, we’ll offer some opinions on the situation, as well as share our own views on how we’re reacting at Grafana Labs.

It seems like hardly a week goes by without news of a security breach. Cyberattacks are becoming more frequent and more severe, costing businesses $600 billion per year according to the 2018 Economic Impact of Cybercrime report. Without a strong security policy in place, businesses risk falling victim to new threats while losing the trust of their customers.

Software often seems like a benign version of Game of Thrones, in which any dominant or ascending technology/methodology is constantly challenged by newer and more attractive rivals. So as soon as microservices entered the mainstream, it didn’t take long until some developers saw it as flawed, and proposed nanoservices as a replacement. In this article, we ask why the move to breaking down software into smaller and smaller pieces is a good idea.

Bloomberg is best known as a media company with its news destination site, its award-winning magazine Bloomberg Businessweek, and its daily 24-7 social media program, Tic Toc, on Twitter. But the main product for the 38-year-old company is actually Bloomberg Terminal, a software system that aggregates real-time market data and delivers financial news to more than 325,000 subscribers around the world.

The ELK Stack is a great open-source stack for log aggregation and analytics. It stands for Elasticsearch (a NoSQL database and search server), Logstash (a log shipping and parsing service), and Kibana (a web interface that connects users with the Elasticsearch database and enables visualization and search options for system operation users). With a large open-source community, ELK has become quite popular, and it is a pleasure to work with.

When managing cloud-native applications, it’s essential to have end-to-end visibility into what’s happening at any given time. This is especially true because of the distributed and dynamic nature of cloud-native apps, which are often deployed using ephemeral technologies like containers and serverless functions.

At the end of Part 1, the end result was a set of polygons that represented each Cassandra node in a Kubernetes statefulset. The cAdvisor-based metrics CPU/Memory/Disk utilization are scraped by Prometheus. This second tutorial will focus on a rollup of multiple Cassandra clusters running inside Kubernetes. We will end up with three dashboards tied together to provide an overview of our Cassandra clusters.