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Hands-On Salesforce Data Cloud - Helion

Hands-On Salesforce Data Cloud
ebook
Autor: Joyce Kay Avila
ISBN: 9781098147822
stron: 450, Format: ebook
Data wydania: 2024-08-09
Księgarnia: Helion

Cena książki: 220,15 zł (poprzednio: 255,99 zł)
Oszczędzasz: 14% (-35,84 zł)

Dodaj do koszyka Hands-On Salesforce Data Cloud

Tagi: Inne

Learn how to implement and manage a modern customer data platform (CDP) through the Salesforce Data Cloud platform. This practical book provides a comprehensive overview that shows architects, administrators, developers, data engineers, and marketers how to ingest, store, and manage real-time customer data.

Author Joyce Kay Avila demonstrates how to use Salesforce's native connectors, canonical data model, and Einstein's built-in trust layer to accelerate your time to value. You'll learn how to leverage Salesforce's low-code/no-code functionality to expertly build a Data Cloud foundation that unlocks the power of structured and unstructured data. Use Data Cloud tools to build your own predictive models or leverage third-party machine learning platforms like Amazon SageMaker, Google Vertex AI, and Databricks.

This book will help you:

  • Develop a plan to execute a CDP project effectively and efficiently
  • Connect Data Cloud to external data sources and build out a Customer 360 Data Model
  • Leverage data sharing capabilities with Snowflake, BigQuery, Databricks, and Azure
  • Use Salesforce Data Cloud capabilities for identity resolution and segmentation
  • Create calculated, streaming, visualization, and predictive insights
  • Use Data Graphs to power Salesforce Einstein capabilities
  • Learn Data Cloud best practices for all phases of the development lifecycle

Dodaj do koszyka Hands-On Salesforce Data Cloud

 

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Dodaj do koszyka Hands-On Salesforce Data Cloud

Spis treści

Hands-On Salesforce Data Cloud eBook -- spis treści

  • 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
    • OReilly 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
      • Todays 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 CloudTriggered 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 CloudTriggered 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

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