Data Cloud Consultant (Data 360) with Real-World Scenarios

Explore how the data cloud is a hyperscale, natively built platform that connects and harmonizes data across Salesforce orgs to deliver a 360-degree customer view and actionable insights.

Identify and connect data sources from crm clouds and external systems, then ingest, harmonize, and unify data to form a 360-degree customer view, with ai insights and activation.

explore how data cloud builds a unified customer profile from fragmented data using identity resolution, preserving data lineage and activating insights back to sources.

Explore data warehouse, data lake, and data lakehouse, where structured data supports analysis, raw data fuels flexibility, and data cloud unifies both for all users.

Explore data 360 provisioning options for single or multi-org setups, including data cloud one architecture, home vs companion orgs, and governance implications.

Explore how Data Cloud sandboxes operate as metadata-only environments, with no data replication, enabling testing, training, and development, while deployments move via change set, data kit, or the CLI.

Explore Data360 with data explorer, profile explorer, and query editor to inspect data lake and data graphs, apply filters, generate charts, and run read-only sql queries in workspaces.

Sign up for the Salesforce developer edition and confirm your account, then access the data cloud setup to provision your data cloud instance with admin or permission set access.

Manage data cloud access with profiles and permission sets, assign system administrator roles, and apply data cloud architect or activation roles to control read, create, and edit permissions.

Learn how data spaces in the data cloud create logical partitions for profile unification, insights, and marketing. Map ingested data to spaces using prefixes and control access with permission sets.

Post your review in the comment section to help others learn about this data 360 fundamentals with real-world scenarios course. Your feedback fuels motivation and guides future content improvements.

Explore the data model foundation by defining entities, attributes, relationships, and cardinality; learn how entities map to tables with lookup and master-detail connections, including one-to-many and many-to-many relationships.

Understand the Salesforce data cloud data model, focusing on standard entities and DMOs, and how subject areas simplify mapping, identity resolution, and 360-degree customer profiles.

Data cloud auto-detects data types during ingestion of data streams, and harmonization requires matching DLO and DMO types; plan and validate offline to avoid post-creation changes.

Prefer the standard DMO over the custom DMO in Salesforce Data 360, and document the rationale when choosing a custom DMO to map to a complex use case.

Explore normalization and denormalization in data cloud: normalization reduces redundancy and improves integrity; denormalization speeds queries for analytics, with the right choice depending on the use case.

Explore primary keys, foreign keys, and cardinality to build strong data relationships. Learn how unique identifiers and links between parent and child tables shape one-to-one and one-to-many connections.

Explore how data ethics governs data collection, processing, storage, and sharing in data cloud with governance controls, privacy data models, and consent api to ensure trust.

Identify and prioritize data sources across systems to fuel the data cloud and data 360, then plan what to bring, why, and how it will be used, and start small.

Create a living data dictionary, your single source of truth, capturing data sources, use cases, primary and foreign keys, relationships, data quality, and system of record for data cloud ingestion.

Discover how DSO, DLO, and DMO define the data 360 model: raw data, enriched data, and harmonized data for unified insights and activation.

Plan data category before ingestion, choosing profile, engagement, or other. Define profile for customer identity and segmentation; use engagement for time-based events with immutable event time; assign other data.

Master primary key design for data360 ingestion by creating immutable keys, avoiding duplicates, and prioritizing the standard customer 360 data model while explicitly defining primary and foreign keys.

Learn the step-by-step data ingestion process in data cloud, from identifying sources and authenticating connectors to selecting objects, defining data stream properties, optional transformations, data space, and refresh settings.

Choose bulk for large data volumes and uploads, or streaming for real-time insights, and examine look back windows, delivery methods, and refresh options for Salesforce CRM, S3, and ingestion API.

Explore the data ingestion flow from source identification to data stream, data space, DSO, DLO, and transformations with formula fields, including data categorization and standardization.

Learn how data kits package and migrate data cloud configurations, combining data streams, bundles, and models; choose between standard and DevOps data kits to deploy and maintain multi-version setups.

Engage in a hands-on activity to get your org ready by provisioning the data cloud and installing a prepared package for crm and ecommerce data use cases.

Begin data ingestion from the Salesforce CRM using the data cloud service bundle, configure contact, account, and case objects, create formula fields, and deploy multiple data streams with full refresh.

Ingest customer profiles, orders, and line items from AWS S3 using the data cloud, configuring the S3 connector, creating data lake objects, and defining formula keys; validate with data explorer.

Discover how the ingestion API powers data cloud ingestion with streaming and bulk patterns, and learn to configure connectors, YAML schemas, data streams, and OAuth secured access.

Transform denormalized data into normalized forms using batch or streaming data transforms, enable mapping to party identification with properly defined targets and identification types.

Learn to perform batch data transformation to extract driving license and membership numbers from the booking object, clean duplicates, and load into a data lake for party identification.

Explore hands-on data transformation by re-ingesting the same order feed with different settings to set the primary key to store number and map to a new data lake object.

Map ingested data from multiple sources to the canonical data 360 model, creating a unified customer profile. Define relationships and cardinality to enable identity resolution, segmentation, and activation.

Assess prerequisites with a data dictionary and field-level checks; design and configure mapping for the customer 360 data model using standard, custom, or hybrid models to ensure unification and activation.

Navigate the data cloud mapping workflow by mapping data lake objects to data model objects. Validate the data space, choose standard or custom models, and review relationships.

Map Salesforce CRM and e-commerce data to the customer 360 data model, create a booking DMO, and establish relationships to the individual while validating field mappings.

Map ecommerce data from AWS S3 streams by selecting the ecommerce customer profile and sales order objects, mapping primary keys and custom fields, and linking orders to individuals and stores.

Explore how identity resolution in data 360 links sources and unifies customer profiles across multiple data sources using matching and reconciliation rules to create a traceable, unified profile.

Identify data sources and assess data quality, configure strict or relaxed match rules, apply reconciliation, and iteratively validate unified profiles across systems using data explorer tools.

Learn how identity resolution combines data from multiple sources into a unified profile using matching and reconciliation rules, preserving source relationships and building a single customer view.

Discover Salesforce data 360's pre-built matching rules for first name, last name, email, phone, and addresses, and learn how to customize with exact, exact normalized, and fuzzy match methods.

Explore how identity resolution evolves data into unified individual and unified link DMOs, linking source records via unified link objects to a traceable, one-to-many keyring model.

Configure identity resolution by creating a rule set for the data space and adding fuzzy name and normalized email matches, birth date, to unify individual records into a unified profile.

Evaluate identity resolution results by examining matching and consolidation rates, distinguishing anonymous unified profiles from unified individual profiles, and inspecting processing history in the profile explorer.

Review identity resolution outcomes from 220 source profiles, producing 201 unified profiles, and validate results using data explorer and profile explorer within the data cloud framework.

Master identity resolution by avoiding overgrouping and undergrouping; use multiple matching criteria (email, phone, name, address) and map disparate identifiers into a common field for accurate unified records.

Discover how data 360 insights turn unified data into actionable business value by analyzing metrics and dimensions, exploring customer behavior, and visualizing with BI tools.

Explore calculated and streaming insights in data 360, differentiating batch historical analytics from real-time, event-driven insights, with practical use cases, time windows, and activation considerations.

Discover two ways to create Data360 insights: use the visual insight builder to define measures, dimensions, and filters without sql, or write complex sql in the sql editor.

Learn to write basic sql with select, from, where and measures; master inner, left, right, and full joins, plus functions case, if null, rank, and window for data 360 insights.

Create calculated insights with the visual builder and sql to compute grand total, average, min, max, and transaction counts from sales orders for customer-level insights.

Transform map data into actionable audience segments by combining geographic, demographic, psychographic, and behavioral attributes to target and analyze customers effectively in data 360. Apply segmentation to regional targets.

Navigate Data360 UI to create segments with the segment tab, new button, and visual builder; define properties, select the entity, and choose publish type with lookback windows.

Learn how the segmentation canvas uses direct and related attributes and a container to define segmentation logic, targeting a specific population with defined criteria.

Explore the segmentation canvas, define inclusion and exclusion, use aggregation to quantify results, and apply date, numeric, text, and boolean operators with containers and between containers.

Navigate segmentation with container path from a target entity to related data, then use calculated insights, matrices, and dimensions to build data-driven, efficient segments.

Create customer segments through segmentation using the visual builder: identify store 5545 customers with at least two purchases, and build high spenders with and/or criteria over a two-year lookback.