IBM Cloud Pak for Data: An enterprise platform to operationalize data, analytics, and AI

Cover
Title Page
Copyright and Credits
Contributors
About the reviewers
Table of Contents
Preface
Section 1: The Basics
Chapter 1: The AI Ladder – IBM's Prescriptive Approach
	Market dynamics and IBM's Data and AI portfolio
	Introduction to the AI ladder
		The rungs of the AI ladder
	Collect – making data simple and accessible
	Organize – creating a trusted analytics foundation
		People empowering your data citizens
	Analyze – building and scaling models with trust and transparency
	Infuse – operationalizing AI throughout the business
		Customer service
		Risk and compliance
		IT operations
		Financial operations
		Business operations
		The case for a data and AI platform
	Summary
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
		Key use cases
		Customer use case: AI claim processing
		Customer use case: data and AI platform
	Cloud Pak for Data: additional details
		An open ecosystem
		Premium IBM cartridges and third-party services
		Industry accelerators
		Packaging and deployment options
	Red Hat OpenShift
	Summary
Section 2: Product Capabilities
Chapter 3: Collect – Making Data Simple and Accessible
	Data – the world's most valuable asset
		Data-centric enterprises
	Challenges with data-centric delivery
	Enterprise data architecture
		NoSQL data stores – key categories
	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
Chapter 4: Organize – Creating a Trusted Analytics Foundation
	Introducing Data Operations (DataOps)
	Organizing enterprise information assets
	Establishing metadata and stewardship
		Business metadata components
		Technical metadata components
	Profiling to get a better understanding of your data
	Classifying data for completeness
		Automating data discovery and business term assignment
	Enabling trust with data quality
		Steps to assess data quality
		DataOps in action
		Automation rules around data quality
	Data privacy and activity monitoring
	Data integration at scale
		Considerations for selecting a data integration tool
		The extract, transform, and load (ETL) service in Cloud Pak for Data
		Advantages of leveraging a cloud-native platform for ETL
	Master data management
	Extending MDM toward a Digital Twin
	Summary
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
		Data science tools for a diverse data science team
		Distributed AI
		Establishing a collaborative environment and building AI models
		Choosing the right tools to use
		ModelOps – Deployment phase
		ModelOps – Monitoring phase
		Streaming data/analytics
		Distributed processing
	Summary
Chapter 6: Multi-Cloud Strategy and Cloud Satellite
	IBM's multi-cloud strategy
	Supported deployment options
		Managed OpenShift
		AWS Quick Start
		Azure Marketplace and quickstart templates
	Cloud Pak for Data as a Service
		Packaging and pricing
	IBM Cloud Satellite
	A data fabric for a multi-cloud future
	Summary
Chapter 7: IBM and Partner Extension Services
	IBM and third-party extension services
	Collect extension services
		Db2 Advanced
		Informix
		Virtual Data Pipeline
		EDB Postgres Advanced Server
		MongoDB Enterprise Advanced
	Organize extension services
		DataStage
		Information Server
		Master Data Management
		Analyze cartridges – IBM Palantir
	Infuse cartridges
		Cognos Analytics
		Planning Analytics
		Watson Assistant
		Watson Discovery
		Watson API Kit
	Modernization upgrades to Cloud Pak for Data cartridges
		Extension services
	Summary
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
Section 3: Technical Details
Chapter 9: Technical Overview, Management, and Administration
	Technical requirements
	Architecture overview
		Characteristics of the platform
		Technical underpinnings
		The operator pattern
		The platform technical stack
	Infrastructure requirements, storage, and networking
		Understanding how storage is used
		Networking
	Foundational services and the control plane
		Cloud Pak foundational services
		Cloud Pak for Data control plane
		Management and monitoring
	Multi-tenancy, resource management, and security
		Isolation using namespaces
		Resource management and quotas
		Enabling tenant self-management
	Day 2 operations
		Upgrades
		Scale-out
		Backup and restore
	Summary
	References
Chapter 10: Security and Compliance
	Technical requirements
	Security and Privacy by Design
		Development practices
		Vulnerability detection
		Delivering security assured container images
	Secure operations in a shared environment
		Securing Kubernetes hosts
		Security in OpenShift Container Platform
		Namespace scoping and service account privileges
		RBAC and the least privilege principle
		Workload notification and reliability assurance
		Additional considerations
		Encryption in motion and securing entry points
		Encryption at rest
		Anti-virus software
	User access and authorizations
		Authentication
		Authorization
		User management and groups
		Securing credentials
	Meeting compliance requirements
		Configuring the operating environment for compliance
		Auditing
		Integration with IBM Security Guardium
	Summary
	References
Chapter 11: Storage
	Understanding the concept of persistent volumes
		Kubernetes storage introduction
		Types of persistent volumes
		In-cluster storage
		Optimized hyperconverged storage and compute
		Separated compute and storage Nodes
		Provisioning procedure summary
	Off-cluster storage
		NFS-based persistent volumes
	Operational considerations
		Continuous availability with in-cluster storage
		Data protection – snapshots, backups, and active-passive disaster recovery
		Quiescing Cloud Pak for Data services
		Db2 database backups and HADR
		Kubernetes cluster backup and restore
	Summary
	Further reading
Chapter 12: Multi-Tenancy
	Tenancy considerations
		Designating tenants
		Organizational and operational implications
	Architecting for multi-tenancy
		Achieving tenancy with namespace scoping
		Ensuring separation of duties with Kubernetes RBAC and separation of duties with operators
		Securing access to a tenant instance
		Choosing dedicated versus shared compute nodes
	Reviewing the tenancy requirements
		Isolating tenants
		Tenant security and compliance
		Self-service and management
		A summary of the assessment
	In-namespace sub-tenancy with looser isolation
		Approach
		Assessing the limitations of this approach
	Summary
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