Book Image

IBM Cloud Pak for Data

By : Hemanth Manda, Sriram Srinivasan, Deepak Rangarao
3 (1)
Book Image

IBM Cloud Pak for Data

3 (1)
By: Hemanth Manda, Sriram Srinivasan, Deepak Rangarao

Overview of this book

Cloud Pak for Data is IBM's modern data and AI platform that includes strategic offerings from its data and AI portfolio delivered in a cloud-native fashion with the flexibility of deployment on any cloud. The platform offers a unique approach to addressing modern challenges with an integrated mix of proprietary, open-source, and third-party services. You'll begin by getting to grips with key concepts in modern data management and artificial intelligence (AI), reviewing real-life use cases, and developing an appreciation of the AI Ladder principle. Once you've gotten to grips with the basics, you will explore how Cloud Pak for Data helps in the elegant implementation of the AI Ladder practice to collect, organize, analyze, and infuse data and trustworthy AI across your business. As you advance, you'll discover the capabilities of the platform and extension services, including how they are packaged and priced. With the help of examples present throughout the book, you will gain a deep understanding of the platform, from its rich capabilities and technical architecture to its ecosystem and key go-to-market aspects. By the end of this IBM book, you'll be able to apply IBM Cloud Pak for Data's prescriptive practices and leverage its capabilities to build a trusted data foundation and accelerate AI adoption in your enterprise.
Table of Contents (17 chapters)
1
Section 1: The Basics
4
Section 2: Product Capabilities
11
Section 3: Technical Details

Off-cluster storage

In some situations, enterprises prefer to fully separate compute and storage, or even use a centralized storage management solution that is independently operated, to support the provisioning requirements of multiple clusters. This is quite similar to the separated compute and storage (Figure 11.3) model we described previously, except that the storage cluster is not part of the same OpenShift cluster. This also enables enterprises to leverage their existing investments in storage management solutions outside of Kubernetes.

It is important to note that the use of PVCs, with their abstraction of the location of physical storage, makes it possible for Kubernetes applications to seamlessly work in this case as well. Essentially, Cloud Pak for Data services mount these volumes without having to be know if the storage is external to the OpenShift cluster. Just selecting alternative storage classes during installation would make this possible.

In this deployment...