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.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the color images
Conventions used
Get in touch
Share Your Thoughts
1
Section 1: The Basics
Section 1: The Basics
Free Chapter
2
Chapter 1: The AI Ladder – IBM's Prescriptive Approach
Chapter 1: The AI Ladder – IBM's Prescriptive Approach
Market dynamics and IBM's Data and AI portfolio
Introduction to the AI ladder
Collect – making data simple and accessible
Organize – creating a trusted analytics foundation
Analyze – building and scaling models with trust and transparency
Infuse – operationalizing AI throughout the business
Summary
3
Chapter 2: Cloud Pak for Data: A Brief Introduction
Chapter 2: Cloud Pak for Data: A Brief Introduction
The case of a data and AI platform – recap
Overview of Cloud Pak for Data
Exploring unique differentiators, key use cases, and customer adoption
Cloud Pak for Data: additional details
Red Hat OpenShift
Summary
4
Section 2: Product Capabilities
Section 2: Product Capabilities
5
Chapter 3: Collect – Making Data Simple and Accessible
Chapter 3: Collect – Making Data Simple and Accessible
Data – the world's most valuable asset
Challenges with data-centric delivery
Enterprise data architecture
Data virtualization – accessing data anywhere
Data virtualization versus ETL – when to use what?
Platform connections – streamlining data connectivity
Data estate modernization using Cloud Pak for Data
Summary
6
Chapter 4: Organize – Creating a Trusted Analytics Foundation
Chapter 4: Organize – Creating a Trusted Analytics Foundation
Introducing Data Operations (DataOps)
Organizing enterprise information assets
Establishing metadata and stewardship
Profiling to get a better understanding of your data
Classifying data for completeness
Enabling trust with data quality
Data privacy and activity monitoring
Data integration at scale
Master data management
Extending MDM toward a Digital Twin
Summary
7
Chapter 5: Analyzing: Building, Deploying, and Scaling Models with Trust and Transparency
Chapter 5: Analyzing: Building, Deploying, and Scaling Models with Trust and Transparency
Self-service analytics of governed data
BI and reporting
Predictive versus prescriptive analytics
Understanding AI
AI life cycle – Transforming insights into action
AI governance: Trust and transparency
Automating the AI life cycle using Cloud Pak for Data
Summary
8
Chapter 6: Multi-Cloud Strategy and Cloud Satellite
Chapter 6: Multi-Cloud Strategy and Cloud Satellite
IBM's multi-cloud strategy
Supported deployment options
Cloud Pak for Data as a Service
IBM Cloud Satellite
A data fabric for a multi-cloud future
Summary
9
Chapter 7: IBM and Partner Extension Services
Chapter 7: IBM and Partner Extension Services
IBM and third-party extension services
Collect extension services
Organize extension services
Infuse cartridges
Modernization upgrades to Cloud Pak for Data cartridges
Summary
10
Chapter 8: Customer Use Cases
Chapter 8: Customer Use Cases
Improving health advocacy program efficiency
Voice-enabled chatbots
Risk and control automation
Enhanced border security
Unified Data Fabric
Financial planning and analytics
Summary
11
Section 3: Technical Details
Section 3: Technical Details
12
Chapter 9: Technical Overview, Management, and Administration
Chapter 9: Technical Overview, Management, and Administration
Technical requirements
Architecture overview
Infrastructure requirements, storage, and networking
Foundational services and the control plane
Multi-tenancy, resource management, and security
Day 2 operations
Summary
References
13
Chapter 10: Security and Compliance
Chapter 10: Security and Compliance
Technical requirements
Security and Privacy by Design
Secure operations in a shared environment
User access and authorizations
Meeting compliance requirements
Summary
References
14
Chapter 11: Storage
Chapter 11: Storage
Understanding the concept of persistent volumes
Off-cluster storage
Operational considerations
Summary
Further reading
15
Chapter 12: Multi-Tenancy
Chapter 12: Multi-Tenancy
Tenancy considerations
Architecting for multi-tenancy
Reviewing the tenancy requirements
In-namespace sub-tenancy with looser isolation
Summary
Why subscribe?
16
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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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