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)
Section 1: The Basics
Section 2: Product Capabilities
Section 3: Technical Details


This chapter continued covering the foundational concepts we introduced in Chapter 9, Technical Overview, Management, and Administration, in terms of what it means to operate Cloud Pak for Data in a multi-tenanted fashion, with security and reliability in mind.

We elaborated on the key tenancy requirements, and then focused on the namespace per tenant approach as the best practice for supporting multiple Cloud Pak for Data tenants within the same shared OpenShift Kubernetes cluster. We also described how this recommended approach, along with important organizational structures, helps achieve the key tenancy requirements, starting with how Kubernetes namespaces and OpenShift projects, as a concept, help with enforcing tenant isolations. This chapter also reinforced the importance of the Operator pattern to help in improving the security posture and the different capabilities in the control plane that support tenancy, along with assurances of security and meeting performance...