Salesforce Data Cloud Masterclass

Learn data ethics in the data cloud by honoring customer opt-out choices, ensuring transparency, and carefully handling sensitive data like PII while sharing only with trusted partners.

Learn how data cloud harmonizes disparate data to unify customer profiles, derive insights, build segments, and activate data at scale with out-of-the-box connectors and AI tools.

Explore industry use cases for Salesforce Data Cloud, from financial services with unified profiles and life-event segmentation to healthcare with a unified health score and alerts for intervention.

Learn the big picture of implementing Salesforce data cloud, focusing on data preparation—provisioning, integrations, ingestion, mapping, and identity resolution—and data consumption via unified profiles and insights.

Discover how the customer 360 data model, Data Cloud's standard data model, standardizes data across sources by organizing it into subject areas, demos, and attributes like contact point email.

Learn to apply the consent API to delete a unified profile’s data from data cloud and connected systems, with 30/60/90 day reprocessing for full deletion.

Master harmonization, activation target, data stream, and segmentation within the data cloud overview. Learn how harmonization maps ingested data to the customer 360 data model.

Learn to set up and provision the data cloud as an admin, enable data cloud, manage users and permissions, and configure data mapping, models, and streams for Salesforce integration.

Update admin user during provisioning, provision data cloud app, create user profiles, assign permission sets, configure data connections, ingest data, and set identity resolution, calculated inside segments, and activation targets.

Navigate Data Cloud for marketing permission sets across admin, aware specialist, manager, and specialist profiles, detailing access to segments, activations, activation targets, and data streams.

Explore data cloud connectors for Salesforce apps and external sources; starter data bundles provide predefined objects like email, Mobile Connect, and Mobile Push for quick ingestion into a unified profile.

Data spaces organize data in the data cloud as logical partitions by region or brand, allowing ingestion from multiple sources and context-based segmentation, activation, and data actions.

Set up and explore the Salesforce data cloud through a guided lab, configuring admin permissions and connecting data sources. Examine data streams, data explorer, and calculated insights for segments.

Explore data kits and Salesforce packages in data cloud, including managed versus unmanaged types, artifacts, and how package manager streamlines setup with streams, calculated insights, and a mapped data model.

Understand normalized data as multiple related tables that reduce redundancy and ensure incoming data maps to the data model, while denormalized data merges into a single table to speed retrieval.

Explore data ingestion streams feeding the data cloud from multiple sources and compare data lake, data warehouse, and data lake house for structured, semi-structured, and unstructured data.

Explore the data ingestion flow from data streams to data source objects, apply minor transformations, then map to data lake objects and the customer 360 data model for analytics.

Compare data ingestion timings across connectors, detailing lookback windows, batch and near real-time delivery, latency, and refresh modes for Marketing Cloud, Salesforce CRM, cloud storage, and B2C commerce.

Data object type categories define what data enters cloud: profile, engagement, or other; once chosen, the category cannot be changed, and streams use field types like text, date, or boolean.

Discover how formulas enrich source data with functions like concat, if, proper, and replace, then compare streaming versus batch data transforms for data lake and data model objects.

Map ingested data to the data cloud using the customer 360 model to enable unification, segmentation, activation, and analytics. Define data model objects such as contact point and party identification.

Create data streams using the Salesforce CRM bundle, apply a US-based formula field, and map lead, contact, and account fields to the data model within the data cloud lab.

Review data ingestion and transformation from CRM sources and cloud storage, including staging to the DSO, transforming, and mapping to Delos data lake objects and DMOs for analytics.

Understand ingestion basics in the Salesforce Data Cloud Masterclass, including AWS S3 errors, bundles and objects, data field values, data spaces, and full refresh vs upsert.

Learn how to create a single customer identity by connecting data from multiple sources, mapping to a standard data model, and matching and resolving records in data cloud without code.

Create a unified customer profile by applying identity resolution to reconcile records across data sources using rule sets, producing a single source of truth with accurate, consolidated data.

Learn to build identity resolution in the Salesforce Data Cloud Masterclass by creating rule sets, configuring match rules and reconciliation strategies, and validating results through the Profile Explorer.

Create a unified profile from distributed customer data across service, marketing, commerce, and loyalty sources. Use normalization and fuzzy name matching to link records into a single unified id.

Create identity resolution rule set in data cloud, enable jobs, link sources to unified profiles, and configure match rules with fuzzy name and normalized email, phone, address, using source-priority reconciliation.

Clarify identity resolution concepts in the Salesforce data cloud, including unified profile, match rules, and reconciliation rules. Differentiate golden record and keyring for a comprehensive customer view.

Explore calculated insights in data cloud to query, transform, and create multidimensional metrics for segmentation and personalization, using attributes, measures, and dimensions.

Master streaming insights for real time and near real time analytics, time series analytics, data actions, identity resolution, and anomaly detection with insights builder, SQL, and Tableau via JDBC.

Create a data stream for case object, map fields, and build calculated insights with Insight Builder to count cases by status per unified individual; publish and view in Data Explorer.

Learn segmentation basics in the Salesforce data cloud masterclass, from creating unified individuals through identity resolution to filtering segments that target or analyze customers for activation across platforms.

Learn to create and activate segments in data cloud, define activation targets, and choose between standard and rapid publish with their lookback window and target scope.

Publish segments from your data model to activation platforms by associating each segment with an activation target such as Marketing Cloud, then activate to deliver to the target.

Explore activation targets as locations to send segment data during activation, including Marketing Cloud, Commerce Cloud, external platforms like Meta, Google Ads, AppExchange, and cloud storage with access keys.

Data actions enable near real-time responses to streaming insights by triggering emails, platform events, or webhooks, based on defined rules across channels.

Identify US-based leads with missing zip codes by creating a 'missing zip code' segment and activating it to the Data Cloud for outreach and profile enrichment.

Learn to build data streams from three Amazon S3 files—contact home, customer profile, and order pay details—create a unified profile, define a date-range segment, and publish to Amazon S3.

Set up an Amazon S3 connector in the data cloud, test it, and deploy data streams to ingest contact home, customer profile, and order pay details (profile and engagement data).

Build a Salesforce data cloud data model by mapping data lake objects to individual, account contact, and contact point DMOs, establishing IDs, phones, emails, addresses, and custom relationships.

Create a unified profile through identity resolution by configuring a rule set with fuzzy name and normalized email, setting source priorities, and running the rule set to consolidate records.

Create a standard segment in Salesforce Data Cloud to identify customers who placed orders within a date range, using order date and the between operator, then save the segment.

Create an activation target pointing to S3 with CSV format and name it order date activation, then add attributes like order date, first name, last name, and email, and publish.

Verify the activation setup and its successful publication to the activation target, then review publish history and data explorer records, including order date, within Salesforce 360 segments and S3 metadata.

Explore the Salesforce data cloud consultant exam structure, including solution overview, administration, data ingestion and modeling, identity resolution, segmentation and insights, and activation, with focus areas for each section.

Ingest batch or streaming data with MuleSoft connectors and transform it into a unified customer profile. Explore activation, insights, and industry use cases while respecting consent and data ethics.

identify permission sets for visibility and provisioning the data cloud app; create user profiles, set up data connections, and note connector limits, data spaces, and data kits.

Learn the data ingestion flow, map data streams to profile, engagement, or other categories, and apply data mapping, formulas, transformations, and identity resolution.

Understand identity resolution and the resulting objects, such as unified individual and unified contact points, plus reconciliation rules, source priority, and consolidation rate.

Master segmentation and insights in salesforce data cloud by comparing calculated and streaming insights, managing dimensions and measures, and applying filters to build segments with standard and rapid timing.

Master the activation process by publishing segments to activation targets, activating contact points and attributes, and using flows to trigger changes from calculated insights, with full or incremental refresh.