Salesforce Data Cloud Architecture: A Complete Guide for Developers
Salesforce Data Cloud (now Data 360) provides a scalable, real-time data platform that unifies structured and unstructured customer data across sources, enabling developers to build AI-powered applications without data movement.
Core Architecture Layers
Data Ingestion Layer: Connectors ingest data into Data Lake Objects (DLOs) preserving original schemas from CRM, databases, and external systems. Unstructured Data Lake Objects (UDLOs) handle documents, emails, and chat transcripts via chunking, embedding, and vector storage for semantic search.
Data Transformation: Batch/streaming pipelines apply formulas (proper case, trim, concatenation) and SQL transformations. DLOs map to Data Model Objects (DMOs) using the standardized Customer 360 (C360) schema for unified profiles, relationships, and engagement data.
Unified Profile Layer: DMOs create real-time 360° customer views combining structured facts with behavioral signals. Data Spaces partition data by tenant or business unit for governance and scale.
Activation Layer: Developers access unified data via APIs, Einstein AI models, Agentforce actions, and zero-copy sharing with CRM orgs (Home/Companion model). Vector databases power RAG for generative AI.
Developer Objects & APIs
Object TypePurposeCreation Method
DLORaw structured data storageAuto/manual ingestion
UDLOUnstructured file indexingManual + blob store
DMOUnified C360 entitiesMapping from DLOs
SegmentsCustomer cohortsSQL/Planner UI
Calculated InsightsML-derived metricsRecipes + Einstein
Key APIs: REST/SOAP for data ops, Metadata API for objects, Bulk API 2.0 for streaming, Query API (SQL-like) for analytics.
Development Workflow
Setup: Provision Data Cloud org, connect sources via pre-built connectors (Amazon S3, Snowflake, etc.).
Ingest: Create DLOs/UDLOs, validate schemas.
Map & Transform: Build mappings to DMOs using visual mapper or JSON specs.
Build: Use VS Code + Salesforce CLI for scratch orgs, 2GP packages, and Data Cloud metadata deployment.
Test: Leverage sandboxes, Data Cloud One for zero-copy CRM integration.
Deploy: Promote via CI/CD with Apex actions and Flow orchestration.
Developer Best Practices
Use Zero Copy sharing between Data Cloud Home (data processing) and Companion (CRM) orgs.
Optimize queries with partitioning, materialized views, and query profiles.
Secure with Einstein Trust Layer (FedRAMP, GDPR) and row-level access policies.
Scale via Data Spaces for multi-tenant apps; monitor via Event Log and Health Checks.
This architecture eliminates ETL silos, powering real-time AI agents and analytics directly on unified customer data. Developers build once, activate everywhere across Salesforce ecosystem.
more details here:- https://www.itechcloudsolution.com/blogs/salesforce-data-cloud-architecture/
