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Data Cloud

  • Course Content
  • Overview

Salesforce Data Cloud = unify customer data and activate it.

What is Salesforce Data Cloud (Data 360)?

Salesforce Data Cloud (also called Data 360) is Salesforce’s customer data platform that unifies data from Salesforce and external systems into a real-time, trusted customer profile. It brings together structured and unstructured data, resolves identities to create a single view of the customer, and makes that data usable across Salesforce for segmentation, personalization, analytics, and AI. In short, Data Cloud turns scattered customer data into unified, actionable intelligence inside the Salesforce ecosystem.

Who Can Learn Salesforce Data Cloud?

Salesforce Data Cloud can be learned by Salesforce Admins, Developers, Consultants, Business Analysts, Data Analysts, and Marketing/Service professionals who work with customer data and personalization. Admins can focus on configuration, security, ingestion, and segmentation, while Developers and Integration specialists can handle APIs, transformations, and external connectivity. Anyone with Salesforce basics and an interest in data and customer journeys can start learning Data Cloud and grow into roles like Data Cloud Consultant, Data Cloud Admin, or Customer Data Platform Specialist.

Do We Need Coding?

Coding is not required to start with Salesforce Data Cloud because most capabilities can be configured through setup, connectors, data streams, mappings, identity rules, segments, activations, and dashboards. However, coding can be useful for advanced needs such as API-based ingestion, custom integrations, and Apex-based automation. You can become job-ready with strong configuration skills, and later add development skills for deeper integration work.

Where Is It Used in the Real World?

Salesforce Data Cloud is used in real-world scenarios where organizations need a single customer view and real-time personalization. For example, marketing teams use Data Cloud segments to target the right audiences and activate campaigns in channels like email and ads. Service teams use unified profiles to deliver faster, more personalized support. Sales teams use enriched customer insights to prioritize leads and identify next best actions. Data Cloud is also widely used to power AI use cases by providing trusted, unified data for analytics, predictions, and grounded responses.

Why It’s Important

Salesforce Data Cloud is important because modern businesses need unified, clean, and real-time customer data to deliver personalized experiences and reliable AI outcomes. It helps break data silos, improves customer engagement, and enables faster decision-making through insights and activation. For learners, Data Cloud is a high-demand skill because companies are increasingly investing in CDP solutions, personalization, and AI, creating strong career opportunities in data-focused Salesforce roles.

Data Cloud

  • 26 sessions
  • Data Cloud Session 01

    Data Cloud Overview

    • What is Data Cloud (Data 360) and where it fits in Salesforce.
    • Differentiate Data Cloud from core CRM data?
    • Explore key use cases: 360 profile, personalization, analytics, and AI.
    • Review core building blocks: data ingestion, unification, activation, and insights.
    • Walk through a high-level end-to-end architecture diagram.
  • Data Cloud Session 02

    Credit Consumption in Data Cloud

    • How credits work in Data Cloud (compute, storage, queries, activations).
    • Understand which features consume more credits (streams, segments, activations).
    • Use monitoring tools to track credit usage by feature and time.
    • Discuss cost-optimization strategies and best practices.
    • Review real-world examples of controlling spend in projects.
  • Data Cloud Session 03

    Data Cloud Admin Responsibilities

    • Define the role of Data Cloud Admin vs Salesforce Admin.
    • Manage permissions, permission sets, and access control for Data Cloud.
    • Configure system settings: region, encryption, retention.
    • Admin tasks: monitoring jobs, data flows, and system health.
    • Establish governance processes and documentation standards.
  • Data Cloud Session 04

    Data Cloud Setup Fundamentals

    • Enable Data Cloud in org and review prerequisites.
    • Configure initial settings (time zone, data retention, namespaces).
    • Connect Data Cloud to core Salesforce data.
    • Validate setup with a simple ingestion and profile check.
    • Identify common setup issues and how to resolve them.
  • Data Cloud Session 05

    Data Kits and Sandbox Strategy

    • Understand what Data Kits are and when to use them.
    • Use Data Kits to accelerate implementation with prebuilt assets.
    • Explore Data Cloud Sandbox options and use cases.
    • Plan sandbox refresh and data seeding strategies.
    • Best practices for safe testing and training environments.
  • Data Cloud Session 06

    Connectors and Data Sources

    • Review supported connectors (Salesforce CRM, S3, REST, Marketing Cloud, etc.).
    • Configure a connector step-by-step (e.g., CRM or S3).
    • Discuss authentication and security considerations for each source.
    • Plan data ingestion from multiple systems.
    • Lab idea: connect a sample external source and validate records.
  • Data Cloud Session 07

    Data Streams – Ingestion Engine

    • Define Data Streams and their role in ingestion.
    • Create and schedule a Data Stream from a connector.
    • Understand full load vs incremental load patterns.
    • Monitor Data Stream status and error handling.
    • Design ingestion frequency based on business needs.
  • Data Cloud Session 08

    Data Lake Objects (DLOs)

    • Understand what Data Lake Objects are and how they store raw data.
    • Explore schema, fields, and relationships at DLO level.
    • Map Data Streams into DLOs effectively.
    • Discuss partitioning and performance considerations.
    • Plan DLO naming conventions and lifecycle.
  • Data Cloud Session 09

    Unstructured Data Lake Objects (UDLOs)

    • Introduce Unstructured Data Lake Objects and their purpose.
    • Ingest unstructured data (PDFs, docs, web content) into UDLOs.
    • Understand how text extraction and chunking works.
    • Explore use cases: FAQs, manuals, contracts, logs.
    • Connect UDLOs later to AI/RAG scenarios (Einstein/Agentforce).
  • Data Cloud Session 10

    Data Model Objects (DMOs)

    • Define Data Model Objects and the Customer 360 data model.
    • Map DLO fields into DMOs for harmonized entities (Person, Account, etc.).
    • Use standard vs custom DMOs and when to create new ones.
    • Maintain relationships between DMOs (household, account, product).
    • Design a logical data model for a sample business.
  • Data Cloud Session 11

    Data Transformations

    • Understand transformation options (recipes, mapping, calculated fields).
    • Implement transformation pipelines from raw to modeled data.
    • Handle data quality: cleansing, standardization, deduplication support.
    • Plan transformation sequencing and dependencies.
    • Lab idea: build a simple transformation from raw transactions to clean facts.
  • Data Cloud Session 12

    Data Harmonization (Mapping)

    • Learn harmonization concepts: mapping multiple sources to a single model.
    • Configure field mappings from different systems into DMOs.
    • Resolve schema conflicts (data types, naming, semantics).
    • Design a harmonization strategy for multi-source customer data.
    • Validate harmonized output with sample profiles.
  • Data Cloud Session 13

    Identity Resolution

    • Understand identity resolution goals: unify profiles into “one person”.
    • Configure identity rules (deterministic, probabilistic).
    • Use match keys and weighting strategies.
    • Review unified customer profiles after resolution.
    • Monitor and tune identity resolution over time.
  • Data Cloud Session 14

    Data Explorer and Profile Explorer

    • Navigate Data Explorer to inspect ingested and modeled data.
    • Use filters and queries to validate DLO and DMO contents.
    • Work with Profile Explorer to see 360-degree customer views.
    • Drill into related objects and interactions from a profile.
    • Use explorers for troubleshooting and business demos.
  • Data Cloud Session 15

    Data Shares and Data Share Targets

    • Understand Data Shares and why they matter for collaboration.
    • Configure Data Share Targets (e.g., Tableau, Snowflake).
    • Control which data is shared and at what grain.
    • Monitor usage and access to shared datasets.
    • Governance and compliance considerations for external sharing.
  • Data Cloud Session 16

    Data Actions, Targets, and Flows

    • Introduce Data Actions for activating Data Cloud data back into systems.
    • Configure Data Action Targets (Salesforce CRM, Marketing tools, etc.).
    • Build Flows that use Data Cloud segments and attributes.
    • Design real-time vs batch activation patterns.
    • Lab idea: flow that updates CRM records from segment membership.
  • Data Cloud Session 17

    Query Editor Essentials

    • Overview of Query Editor interface in Data Cloud.
    • Build basic SQL-like queries on DLOs and DMOs.
    • Use filters, joins, and aggregations to answer business questions.
    • Save and reuse queries for recurring analysis.
    • Best practices for query performance and limits.
  • Data Cloud Session 18

    Segments – Audience Building

    • Understand Segments and their role in activation and personalization.
    • Create rule-based and behavior-based segments.
    • Use real-time vs batch segmentation approaches.
    • Preview segment membership and validate logic.
    • Design segments for marketing, service, and sales use cases.
  • Data Cloud Session 19

    Activation and Activation Targets

    • Explain what Activation means in Data Cloud lifecycle.
    • Configure Activation Targets (CRM, Marketing Cloud, ad platforms, etc.).
    • Map segment fields to target destination fields.
    • Schedule and monitor activation runs.
    • Handle failure scenarios and incremental activations.
  • Data Cloud Session 20

    Data Graphs and Data Spaces

    • Introduce Data Spaces for multi-business-unit or region isolation.
    • Explain Data Graphs as logical views of related data across objects.
    • Build a Data Graph for customer + product + interaction data.
    • Govern who can access which Data Space and Graph.
    • Use Data Spaces to separate dev/test/prod scenarios.
  • Data Cloud Session 21

    Calculated Insights, Reports, and Dashboards

    • Define Calculated Insights and how they differ from segments.
    • Build insights (CLV, churn risk, engagement scores).
    • Expose Calculated Insights to CRM and analytics tools.
    • Create reports and dashboards using Data Cloud metrics.
    • Use insights for targeting, personalization, and operations.
  • Data Cloud Session 22

    APIs in Data Cloud

    • Overview of key APIs: Consent API, Query API, Ingestion API, Profile API, Connect REST API.
    • Use cases for each API in real-world projects.
    • Authentication and security patterns for Data Cloud APIs.
    • Walk through simple API call examples (e.g., Query API).
    • Plan API governance and rate limit management.
  • Data Cloud Session 23

    Utilizing Apex with Data Cloud

    • Explore options to access Data Cloud data from Apex.
    • Use Connect APIs or external objects patterns where applicable.
    • Design Apex services that react to Data Cloud events/segments.
    • Discuss limitations and best practices for performance.
    • Example scenarios: personalized UI, triggered automations.
  • Data Cloud Session 24

    Einstein Studio in Data Cloud

    • Introduce Einstein Studio and its role with Data Cloud.
    • Connect Data Cloud datasets to Einstein Studio for modeling.
    • Choose model types for predictions (churn, propensity, etc.).
    • Deploy models back into Data Cloud and CRM.
    • Governance and monitoring of model performance.
  • Data Cloud Session 25

    AI in Data Cloud

    • Position AI as a layer on top of unified Data Cloud data.
    • Combine Calculated Insights, segments, and predictions.
    • Use AI for next-best action, personalization, and anomaly detection.
    • Discuss responsible AI: fairness, transparency, and controls.
    • Prepare org for AI-driven use cases and roadmap.
  • Data Cloud Session 26

    Data Cloud for Agentforce and Prompt Builder

    • Use Data Cloud segments and profiles as grounding for Agentforce agents.
    • Connect Data Cloud and ADL (Agentforce Data Library) for RAG scenarios.
    • Feed Calculated Insights into Agent topics and actions.
    • Use Data Cloud datasets as resources in Prompt Builder templates.
    • Design end-to-end scenario: Data Cloud → Prompt → Agent → Activation.
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