Hands-On Salesforce Data Cloud Chorzów

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 efficientlyConnect Data Cloud to external data sources and build out a Customer 360 Data ModelLeverage data sharing capabilities with Snowflake, BigQuery, Databricks, and AzureUse Salesforce Data Cloud capabilities for identity resolution and segmentationCreate calculated, streaming, visualization, and predictive insightsUse Data Graphs to power Salesforce Einstein capabilitiesLearn Data Cloud best practices for all phases of the development lifecycle 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