Introduction: 

This article is a continuation of the series of articles starting with the description of SignalR service. 

In this article, we continue with our study of Azure Stream Analytics from the last article. We were comparing Apache Flink and Kafka with The Azure Stream Analytics and were observing the utilization of Kubernetes to leverage containers for the clusters and running jobs for analysis. 

Windows Azure also hosts Kubernetes for customers. Native Containers are small and fast. They have two characteristics. First, the containers are isolated from each other and from the host in that they even have their own file system. which makes it portable across cloud and os distributions. Second, the immutable container images can be created at build/release time rather than the deployment time of the application since each application doesn't need to be composed with the rest of the application stack nor tied to the production infrastructure environment. Kubernetes extends this idea of app+container all the way where the host can be nodes of a cluster. Kubernetes evolved as an industry effort from the native Linux containers support of the operating system.  It can be considered as a step towards a truly container-centric development environment. Containers decouple applications from infrastructure which separates dev from ops. Containers demonstrate better resource isolation and improved resource utilization. 

At this point, it is important to differentiate Kubernetes from PaaS.  Kubernetes is not a traditional, all-inclusive PaaS. Unlike PaaS that restricts applications, dictates the choice of application frameworks, restricts supported language runtimes, or distinguishes apps from services, Kubernetes aims to support an extremely diverse variety of workloads. If the application has been compiled to run in a container, it will work with Kubernetes. PaaS provides databases, message buses, cluster storage systems but those can run on Kubernetes. There is also no click to deploy service marketplace. Kubernetes does not build user code or deploy it but it facilitates CI workflows to run on it. 

Kubernetes allows users to choose to log, monitoring and alerting Kubernetes also does not require a comprehensive application language or system. It is independent of machine configuration or management. But PaaS can run on Kubernetes and extend its reach to different clouds. 

The section talks about Azure stream analytics cluster this cluster offers a single tenant deployment for complex streaming scenarios clusters can process more than 200 megabytes per second in real time the jobs running on these clusters can leverage the features of they should stream analytics add can directly read from inputs and outputs that a private to organization clusters are built by streaming units (SU) which represent a unit of CPU and memory resources allocated to a cluster. Cluster size can range from 36 SUs to 216 Sus. 

Since the clusters our single tenant dedicated clusters, they can be run in a multi-tenant environment with complete isolation from other tenants. These clusters can be scaled, and a virtual net added to support your jobs to connect to resources securely over private endpoints. These clusters come with zero maintenance cost so that the application can focus only on the streaming jobs with sharing of results with multiple teams.