There are three different abstraction levels of cloud systems--Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). We will see how to use and install Apache Spark on all of these.

The new way to do IaaS is Docker and Kubernetes as opposed to virtual machines, basically providing a way to automatically set up an Apache Spark cluster within minutes. This will be covered in Chapter 14, Apache Spark on Kubernetes. The advantage of Kubernetes is that it can be used among multiple different cloud providers as it is an open standard and also based on open source.

You even can use Kubernetes in a local data center and transparently and dynamically move workloads between local, dedicated, and public cloud data centers. PaaS, in contrast, takes away from you the burden of installing and operating an Apache Spark cluster because this is provided as a service.

There is an ongoing discussion whether Docker is IaaS or PaaS but, in our opinion, this is just a form of a lightweight preinstalled virtual machine. We will cover more on PaaS in Chapter 13, Apache Spark with Jupyter Notebooks on IBM DataScience Experience. This is particularly interesting because the offering is completely based on open source technologies, which enables you to replicate the system on any other data center.

One of the open source components we'll introduce is Jupyter notebooks, a modern way to do data science in a cloud based collaborative environment. But in addition to Jupyter, there is also Apache Zeppelin, which we'll cover briefly in Chapter 14, Apache Spark on Kubernetes.