Foreword
Preface
The Year of Data Cloud
Who Is This Book For?
Goals of the Book
Navigating the Book
Code Examples
Conventions Used in This Book
OβReilly Online Learning
How to Contact Us
Acknowledgments
1. Salesforce Data Cloud Origins
Evolution of the Salesforce Data Cloud Platform
Where Salesforce Data Cloud Fits in the Salesforce Tech Stack
Where the Customer Data Platform Fits in the Martech Stack
Todayβs Modern Martech Stack
The Future of the Martech Stack
The Customer Data Problem
Known Customer Data
Unknown Audience Data
Putting the Pieces Together
Digital Marketing Cookies
First-, Second-, and Third-Party Cookies
The Future of Cookies
Building a First-Party Data Strategy
Extending the First-Party Data Strategy
Data clean room defined
Types of data clean rooms
Data Clean Rooms and Customer Data Platforms Working Together
Customer Data Platform Acquisition Approaches
Build, Buy, or Compose?
Narrowing the Focus
Composable Customer Data Platforms versus a Customer Data Platform Suite
Other Cost and Performance Considerations
Summary
2. Foundations of Salesforce Data Cloud
Special Considerations for Architects
Data-Driven Pattern Use Cases
Considerations for Building a Data-Driven Platform
Salesforce Well-Architected Resources
Data Cloud Technical Capability Map
Data Cloud Key Functional Aspects
General Key Data Concepts
How Data Cloud Works Its Magic
Connecting Multiclouds
Data Spaces
Application Lifecycle Management with Sandboxes
Salesforce AppExchange and Data Kits
Under the Hood: Data Cloud Technical Details
How Data Cloud Is Architected on Amazon Web Services
Storage Layering
Near Real-Time Ingestion and Data Processing
Unique Datastore Features
Data Cloud Data Entities
Starter Data Bundles
Summary
3. Business Value Activities
Achieving Goals with Data and AI Democratization
Building Your Data Cloud Vocabulary
Value Creation Process
Data Cloud Key Value Activities
Data Cloud Enrichments
Large Language Model Grounding Resource for Structured Data
Augmenting Large Language Model Search with Data Graphs and Vector Databases
Data Actions and Data CloudβTriggered Flows
Activation of Segments
Predictive AI Machine Learning Insights
Analytics and Intelligent Data Visualization
Unified Consent Repository
Programmatic Extraction of Data
Bidirectional Data Sharing with External Data Platforms
Linking Custom Large Language Models
Other Key Value Activities
What Data Cloud Is Not
Value by Functional Roles
Value at the Highest Granular Level
Value at the Aggregate Level
Other Critical Functional Roles
Change Management Process: A Necessary Ingredient
Value of a Salesforce Implementation Partner
User Stories and Project Management
Who Decides?
Value in Action: Industry Focus
Travel, Transportation, and Hospitality Industry
Air India
Heathrow Airport
Turtle Bay Resort
Other Industries
Consumer goods and retail industries
Financial services, automotive, health care, life sciences, and manufacturing industries
Nonprofit industry
Similarities among implementations
Summary
4. Admin Basics and First-Time Provisioning
Getting Started
Prework
What You Should Know
Data Cloud User Personas
Data Cloud Admin and Data Cloud User
Data Cloud Marketing Admins
Data Cloud Marketing Managers
Data Cloud Marketing Specialists
Data Cloud Marketing Data Aware Specialists
First-Time Data Cloud Platform Setup
Configuring the Admin User
Provisioning the Data Cloud Platform
Creating Profiles and Configuring Additional Users
Cloning Data Cloud profiles
Creating new Data Cloud users
Connecting to Relevant Salesforce Clouds
Salesforce customer relationship management connections
Marketing Cloud connection
Salesforce B2C Commerce Cloud connection
Marketing Cloud Account Engagement connection
Marketing Cloud Personalization connection
Omnichannel Inventory connection
Beyond the Basics: Managing Feature Access
Creating Data Cloud Custom Permission Sets
Leveraging Data Cloud Sharing Rules
Summary
5. Data Cloud Menu Options
Core Capabilities
Activation Targets
Activations
Calculated Insights
Consumption Cards
Dashboards
Data Action Targets
Data Actions
Data Explorer
Data Graphs
Data Lake Objects
Data Model
Data Share Targets
Data Shares
Data Spaces
Data Streams
Data Transforms
Einstein Studio (aka Model Builder)
Identity Resolutions
Profile Explorer
Query Editor
Reports
Search Index
Segments
Summary
6. Data Ingestion and Storage
Getting Started
Prework
What You Should Know
Viewing Data Cloud Objects via Data Explorer
Ingesting Data Sources via Data Streams
Near Real-Time Ingest Connectors
Salesforce Interactions SDK
Salesforce Web and Mobile Application SDK
Amazon Kinesis
Ingestion API Connector
MuleSoft Anypoint Connector for Salesforce Customer Data Platform
Batch Data Source Ingest Connectors: Salesforce Clouds
Salesforce CRM Connector
Batch Data Sources Ingest Connectors: Cloud Storage
Amazon S3 Storage Connector
Google Cloud Storage Connector
Microsoft Azure Connector
Heroku Postgres Connector
External Platform Connectors
Other Connectors for Batch Ingestion
Ingestion API Connector
MuleSoft Anypoint Connector for Salesforce Customer Data Platform
Secure File Transfer Protocol Connector
Deleting Ingested Records from Data Cloud
Viewing Data Lake Objects
Accessing Data Sources via Data Federation
Summary
7. Data Modeling
Getting Started
Prework
What You Should Know
Data Profiling
Source Data Classification
Data Descriptors
Personal data
Behavioral and engagement data
Attitudinal data
Data Categories
Profile data
Engagement data
Other data
Immutable Date and Datetime Fields
Data Categorization
Salesforce Data Cloud Standard Model
Primary Subject Areas
Extending the Data Cloud Standard Data Model
Adding custom fields to standard data model objects
Adding formula fields and formula expressions
Configuring a qualifier field to support fully qualified keys
Creating custom data model objects
Salesforce objects created from processes
Salesforce Consent Data Model
Global Consent
Engagement Channel Consent
Contact Point Consent
Data Use Purpose Consent
Consent Management by Brand
Consent API
Summary
8. Data Transformations
Getting Started
Prework
What You Should Know
Streaming Data Transforms
Streaming Data Transform Use Cases
Setting Up and Managing Streaming Data Transforms
Streaming Data Transform Functions and Operators
Streaming Transforms versus Batch Transforms
Batch Data Transforms
Batch Data Transform Use Cases
Setting Up and Managing Batch Data Transforms
Batch Data Transform Node Types
Batch Data Transform Limitations and Best Practices
Data Transform Jobs
Summary
9. Data Mapping
Getting Started
Prework
What You Should Know
Data Mapping
Required Mappings
The Field Mapping Canvas
Relationships Among Data Model Objects
DMO relationship status
DMO relationship limits
Using Data Explorer to Validate Results
Summary
10. Identity Resolution
Getting Started
Prework
What You Should Know
Unified profile versus golden record
Party subject area versus Party Identification DMO versus Party field
Identity Resolution Rulesets
Creating Identity Rulesets
Deleting Identity Rulesets
Ruleset Statuses for the Current Job
Ruleset Statuses for the Last Job
Ruleset Configurations Using Matching Rules
Types of Matching Rules
Configuring Identity Resolution Matching Rules
Default Matching Rules
Using Party Identifiers in Matching Rules
Ruleset Configurations Using Reconciliation Rules
Default Reconciliation Rules
Setting a Default Reconciliation Rule
Applying a Different Reconciliation Rule to a Specific Field
Reconciliation Rule Warnings
Anonymous and Known Profiles in Identity Resolution
Identity Resolution Summary
Validating and Optimizing Identity Resolution
Summary
11. Consuming and Taking Action with Data Cloud Data
Getting Started
Prework
What You Should Know
Data Cloud Insights
Creating Insights
Calculated insights
Streaming insights
Real-time insights
Using Insights
Calculated insights benefits
Streaming insights benefits
Data Cloud Enrichments
Related List Enrichments
Copy Field Enrichments
Data Actions and Data CloudβTriggered Flow
Defining a Data Action Target
Platform Event data action target
Webhook data action target
Marketing Cloud data action target
Selecting the Data Action Primary Object
Specifying the Data Action Event Rules
Defining the Action Rules for the Data Action
Enriching Data Actions with Data Graphs
Extracting Data Programmatically
Summary
12. Segmentation and Activation
Getting Started
Prework
What You Should Know
Segmentation and Activation Explained
Defining Activation Targets
Creating a Segment
Segment Builder User Interface
Using the attribute library
Creating filtered segments in containers
Einstein Segment Creation
Segments Built Through APIs
Advanced Segmentation
Einstein lookalike segments
Nested segments
Waterfall segments
Publishing a Segment
Activating a Segment
Contact Points
Activating Direct and Related Attributes
Activation Filters
Calculated Insights in Activation
Activation Refresh Types
Troubleshooting Activation Errors
Segment-Specific Data Model Objects
Segment Membership Data Model Objects from Published Segments
Activation Audience Data Model Objects from Activated Segments
Querying and Reporting for Segments
Best Practices for Segmentation and Activation
Summary
13. The Einstein 1 Platform and the Zero Copy Partner Network
Getting Started
Prework
What You Should Know
Salesforce Einstein
Einstein 1 Platform
Einstein Model Builder
Einstein Prompt Builder
Prompt template types
Ways to invoke Einstein prompts
Einstein Copilot Builder
When to use Einstein Copilot
Standard Copilot actions
Custom Copilot actions
Copilot action assignments
Augmenting Large Language Model Search
Using Data Graphs for Near Real-Time Searches
Using Vector Databases for Unstructured Data
Zero Copy Partner Network
Traditional Methods of Sharing Data
Zero Copy Technology Partners
Amazon
Databricks
Google
Microsoft
Snowflake
Bring Your Own Lake
Bring Your Own Lake federated access (data in)
Bring Your Own Lake data shares (data out)
Important BYOL considerations
Bring Your Own Model
Installing Python Connector and creating a Salesforce-connected app
Connecting the model to Data Cloud to get predictions from your model
Summary
The Road Ahead
Continuing the Learning Journey
Salesforce seasonal releases
Salesforce in-person events
Salesforce partner resources
Salesforce Data Cloud Consultant certification
Keep Blazing the Trail
A. Guidance for Data Cloud Implementation
General Guidelines
Evaluation Phase
Discovery and Design Phases
Implementation and Testing
B. Sharing Data Cloud Data Externally with Other Tools and Platforms
Glossary
Index