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

Multi-tenancy, resource management, and security

OpenShift clusters are considered shared environments in many mature enterprises. They expect to expand such clusters when needed to accommodate more workloads and re-balance available resources. It is far more cost-effective to share the same OpenShift cluster and consolidate management and operations rather than assign a separate cluster to each tenant. Since such clusters include different types of workloads from different vendors, and applications that require sophisticated access management and security practices, a focus on tenancy is important from the start.

The concept of tenancy itself is very subjective. In some situations, dev-test and production installations may be considered different tenants in the same cluster. In other cases, different departments, or different project use cases, may be treated as individual tenants. When this concept is extended to ISVs operating a cluster on behalf of their clients, each of those...