data-cloud-2025

---
name: "data-cloud-2025"
description: "MANDATORY: Always Use Backslashes on Windows for File Paths"
license: "MIT"
---

## CRITICAL GUIDELINES

### Windows File Path Requirements

**MANDATORY: Always Use Backslashes on Windows for File Paths**

When using Edit or Write tools on Windows, you MUST use backslashes (`\`) in file paths, NOT forward slashes (`/`).

Examples:
- WRONG: `D:/repos/project/file.tsx`
- CORRECT: `D:\repos\project\file.tsx`

This applies to:
- Edit tool file_path parameter
- Write tool file_path parameter
- All file operations on Windows systems

### Documentation Guidelines

NEVER create new documentation files unless explicitly requested by the user.

- **Priority**: Update existing README.md files rather than creating new documentation
- **Repository cleanliness**: Keep repository root clean - only README.md unless user requests otherwise
- **Style**: Documentation should be concise, direct, and professional - avoid AI-generated tone
- **User preference**: Only create additional .md files when user specifically asks for documentation

---

# Salesforce Data Cloud Integration Patterns (2025)

## What is Salesforce Data Cloud?

Salesforce Data Cloud is a real-time customer data platform (CDP) that unifies data from any source to create a complete, actionable view of every customer. It powers AI, automation, and analytics across the entire Customer 360 platform.

**Key Capabilities:**
- **Data Ingestion** — Connect 200+ sources (Salesforce, external systems, data lakes)
- **Data Harmonization** — Map disparate data to unified data model
- **Identity Resolution** — Match and merge customer records across sources
- **Real-Time Activation** — Trigger actions based on streaming data
- **Zero Copy Architecture** — Query data in place without moving it
- **AI/ML Ready** — Powers Einstein, Agentforce, and predictive models
- **Vector Database** (GA March 2025) — Store and query unstructured data with semantic search
- **Hybrid Search** (Pilot 2025) — Combine semantic and keyword search for accuracy

## Reference Map

Detailed material lives in `references/`. Load only what the current task needs.

| Topic | File | When to load |
|-------|------|--------------|
| Data ingestion (CDC streaming, batch API, Snowflake/Databricks Zero Copy) | `references/ingestion-patterns.md` | Configuring data sources, importing CSV/SFTP/S3 data, setting up Zero Copy to a warehouse |
| Identity resolution & authentication | `references/identity-resolution.md` | Defining match rules, reconciliation, custom matching, JWT Bearer auth |
| Real-time activation (Flow, Agentforce, Reverse ETL, calculated insights, segmentation, Data Cloud SQL) | `references/activation-patterns.md` | Triggering downstream actions, segmentation, Agentforce grounding, SQL queries |
| Vector Database & semantic/hybrid search | `references/vector-database.md` | Unstructured data indexing, semantic search, Einstein Copilot Search, multi-language search |

## Data Cloud Architecture

```text
┌──────────────────────────────────────────────────────────┐
│                    Data Sources                          │
│  Salesforce CRM │ External Apps │ Data Warehouses │ APIs │
└────────┬─────────────────┬──────────────┬───────────┬────┘
         │                 │              │           │
    ┌────▼─────────────────▼──────────────▼───────────▼────┐
    │         Data Cloud Connectors & Ingestion            │
    │  ├─ Real-time Streaming (Change Data Capture)        │
    │  ├─ Batch Import (scheduled/on-demand)               │
    │  └─ Zero Copy (Snowflake, Databricks, BigQuery)      │
    └────────────────────────┬─────────────────────────────┘
                             │
    ┌────────────────────────▼─────────────────────────────┐
    │            Data Model & Harmonization                │
    │  ├─ Map to Common Data Model (DMO objects)           │
    │  ├─ Identity Resolution (match & merge)              │
    │  └─ Data Transformation (calculated insights)        │
    └────────────────────────┬─────────────────────────────┘
                             │
    ┌────────────────────────▼─────────────────────────────┐
    │         Unified Customer Profile (360° View)         │
    │  ├─ Demographics, Transactions, Behavior, Events     │
    │  └─ Real-time Profile API for instant access         │
    └────────────────────────┬─────────────────────────────┘
                             │
    ┌────────────────────────▼─────────────────────────────┐
    │              Activation & Actions                    │
    │  ├─ Salesforce Flow (real-time automation)           │
    │  ├─ Marketing Cloud (segmentation/journeys)          │
    │  ├─ Agentforce (AI agents)                           │
    │  ├─ Einstein AI (predictions/recommendations)        │
    │  └─ External Systems (reverse ETL)                   │
    └──────────────────────────────────────────────────────┘
```

## Core Workflow

1. **Identify use case** — Ingestion, identity, segmentation, activation, or unstructured/AI search? Pick the matching reference.
2. **Map data sources** — CRM CDC (real-time), external batch (S3/SFTP), or warehouse Zero Copy.
3. **Define DMOs and matching** — Map source fields to Data Model Objects; configure identity resolution match + reconciliation rules.
4. **Build insights / segments** — Calculated insights for KPIs (LTV, churn risk); segments for activation targets.
5. **Activate** — Flow / Platform Events / Agentforce actions / Reverse ETL data actions.
6. **Validate** — Use Data Cloud SQL workbench, check sync logs, monitor identity resolution metrics.

## Best Practices

### Performance
- **Use Zero Copy** for large datasets (>10M records)
- **Batch imports** outside business hours
- **Index frequently queried fields** in Data Cloud
- **Limit real-time triggers** to critical events
- **Cache unified profiles** when possible

### Security
- **Field-level security** applies to Data Cloud queries from Salesforce
- **Data masking** for PII in non-production environments
- **Encryption at rest** and in transit (TLS 1.2+)
- **Audit logging** for all data access
- **Role-based access control** (RBAC) for Data Cloud users

### Data Quality
- **Data validation** before ingestion
- **Deduplication rules** at source and in Data Cloud
- **Data lineage tracking** (know source of each field)
- **Quality scores** for unified profiles
- **Regular data audits** and cleansing

## Resources

- **Data Cloud Documentation:** https://developer.salesforce.com/docs/data/data-cloud-int/guide
- **Zero Copy Partner Network:** https://www.salesforce.com/data/zero-copy/
- **Data Cloud Pricing:** Part of Customer 360 platform, usage-based pricing
- **Trailhead:** "Data Cloud Basics" and "Data Cloud for Developers"