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  • Book Overview & Buying IBM Cloud Pak for Data
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IBM Cloud Pak for Data

IBM Cloud Pak for Data

By : Manda, Srinivasan, Rangarao
5 (7)
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IBM Cloud Pak for Data

IBM Cloud Pak for Data

5 (7)
By: Manda, Srinivasan, Rangarao
Buy this Book

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)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Download the color images
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Conventions used
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Get in touch
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1
Section 1: The Basics
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Section 1: The Basics
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Chapter 1: The AI Ladder – IBM's Prescriptive Approach
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Chapter 1: The AI Ladder – IBM's Prescriptive Approach
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Market dynamics and IBM's Data and AI portfolio
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Introduction to the AI ladder
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Collect – making data simple and accessible
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Organize – creating a trusted analytics foundation
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Analyze – building and scaling models with trust and transparency
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Infuse – operationalizing AI throughout the business
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Summary
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Chapter 2: Cloud Pak for Data: A Brief Introduction
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Chapter 2: Cloud Pak for Data: A Brief Introduction
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The case of a data and AI platform – recap
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Overview of Cloud Pak for Data
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Exploring unique differentiators, key use cases, and customer adoption
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Cloud Pak for Data: additional details
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Red Hat OpenShift
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Summary
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Section 2: Product Capabilities
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Section 2: Product Capabilities
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Chapter 3: Collect – Making Data Simple and Accessible
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Chapter 3: Collect – Making Data Simple and Accessible
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Data – the world's most valuable asset
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Challenges with data-centric delivery
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Enterprise data architecture
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Data virtualization – accessing data anywhere
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Data virtualization versus ETL – when to use what?
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Platform connections – streamlining data connectivity
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Data estate modernization using Cloud Pak for Data
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Summary
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Chapter 4: Organize – Creating a Trusted Analytics Foundation
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Chapter 4: Organize – Creating a Trusted Analytics Foundation
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Introducing Data Operations (DataOps)
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Organizing enterprise information assets
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Establishing metadata and stewardship
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Profiling to get a better understanding of your data
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Classifying data for completeness
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Enabling trust with data quality
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Data privacy and activity monitoring
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Data integration at scale
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Master data management
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Extending MDM toward a Digital Twin
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Summary
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Chapter 5: Analyzing: Building, Deploying, and Scaling Models with Trust and Transparency
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Chapter 5: Analyzing: Building, Deploying, and Scaling Models with Trust and Transparency
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Self-service analytics of governed data
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BI and reporting
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Predictive versus prescriptive analytics
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Understanding AI
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AI life cycle – Transforming insights into action
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AI governance: Trust and transparency
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Automating the AI life cycle using Cloud Pak for Data
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Summary
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Chapter 6: Multi-Cloud Strategy and Cloud Satellite
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Chapter 6: Multi-Cloud Strategy and Cloud Satellite
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IBM's multi-cloud strategy
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Supported deployment options
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Cloud Pak for Data as a Service
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IBM Cloud Satellite
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A data fabric for a multi-cloud future
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Summary
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Chapter 7: IBM and Partner Extension Services
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Chapter 7: IBM and Partner Extension Services
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IBM and third-party extension services
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Collect extension services
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Organize extension services
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Infuse cartridges
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Modernization upgrades to Cloud Pak for Data cartridges
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Summary
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Chapter 8: Customer Use Cases
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Chapter 8: Customer Use Cases
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Improving health advocacy program efficiency
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Voice-enabled chatbots
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Risk and control automation
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Enhanced border security
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Unified Data Fabric
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Financial planning and analytics
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Summary
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Section 3: Technical Details
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Section 3: Technical Details
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Chapter 9: Technical Overview, Management, and Administration
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Chapter 9: Technical Overview, Management, and Administration
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Technical requirements
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Architecture overview
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Infrastructure requirements, storage, and networking
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Foundational services and the control plane
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Multi-tenancy, resource management, and security
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Day 2 operations
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Summary
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References
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Chapter 10: Security and Compliance
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Chapter 10: Security and Compliance
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Technical requirements
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Security and Privacy by Design
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Secure operations in a shared environment
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User access and authorizations
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Meeting compliance requirements
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Summary
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References
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Chapter 11: Storage
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Chapter 11: Storage
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Understanding the concept of persistent volumes
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Off-cluster storage
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Operational considerations
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Summary
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Further reading
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Chapter 12: Multi-Tenancy
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Chapter 12: Multi-Tenancy
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Tenancy considerations
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Architecting for multi-tenancy
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Reviewing the tenancy requirements
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In-namespace sub-tenancy with looser isolation
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Summary
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Why subscribe?
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Other Books You May Enjoy
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Other Books You May Enjoy
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Market dynamics and IBM's Data and AI portfolio

The fact is that every company in the world today is a data company. As the Economist magazine rightly pointed out in 2017, data is the world's most valuable resource and unless you are leveraging your data as a strategic differentiator, you are likely missing out on opportunities.

Simply put, data is the fuel, the cloud is the vehicle, and AI is the destination. The intersection of these three pillars of IT is the driving force behind digital transformation disrupting every company and industry. To be successful, companies need to quickly modernize their portfolio and embrace an intentional strategy to re-tool their data, AI, and application workloads by leveraging a cloud-native architecture. So, cloud platforms act as a great enabler by infusing agility, while AI is the ultimate destination, the so-called nirvana that every enterprise seeks to master.

While the benefits of the cloud are becoming obvious by the day, there are still several enterprises that are reluctant to embrace the public cloud right away. These enterprises are, in some cases, constrained by regulatory concerns, which make it a challenge to operate on public clouds. However, this doesn't mean that they don't see the value of the cloud and the benefits derived from embracing the cloud architecture. Everyone understands that the cloud is the ultimate destination, and taking the necessary steps to prepare and modernize their workloads is not an option, but a survival necessity:

Figure 1.1 – What's reshaping how businesses operate? The driving forces behind digital transformation

Figure 1.1 – What's reshaping how businesses operate? The driving forces behind digital transformation

IBM enjoys a strong Data and AI portfolio, with 100+ products being developed and acquired over the past 40 years, including some marquee offerings such as Db2, Informix, DataStage, Cognos Analytics, SPSS Modeler, Planning Analytics, and more. The depth and breadth of IBM's portfolio is what makes it stand out in the market. With Cloud Pak for Data, IBM is doubling down on this differentiation, further simplifying and modernizing its portfolio as customers look to a hybrid, multi-cloud future.

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