Data Cloud
저자: Sun Ho Kim
1. **Biz Value Stream**
1.1. Data POV
1.1.1. **Connect**
1.1.1.1. Data Streams
1.1.1.1.1. Batch
1.1.1.1.2. Streaming
1.1.1.1.3. Virtual
1.1.2. **Harmonization**
1.1.2.1. Data Mapping
1.1.2.1.1. Data Lake Object
1.1.3. **Unification**
1.1.3.1. ID Resolution
1.1.3.1.1. Method
1.2. Insight & Analysis POV
1.2.1. **Calculated Insights**
1.2.1.1. Change Data Capture
1.2.2. **Streaming Insights**
1.3. Activation POV
1.3.1. Segmentation
2. **Technical POV**
2.1. Security
2.1.1. Customer-end
2.1.1.1. Permission Sets
2.1.1.2. Data Space
2.1.2. Salesforce-end
2.1.2.1. Zero Trust Architecture
2.1.2.2. E2E Encryption
2.1.2.3. Infra As Code
2.1.2.4. Immutable Deployment
2.1.2.5. Just in Time Access
2.1.2.6. Elasticity
2.1.2.7. Observability
3. **AI Aspect**
3.1. BYOM
3.1.1. Einstein Studio
3.1.1.1. ML Predictions
4. Unified Profile
5. **Data Source & Channel**
5.1. Salesforce Core Org
5.1.1. Sales
5.1.2. Service
5.1.3. Other Org
5.2. B2C Commerce
5.3. Marketing Cloud
5.4. Legacy Systems
5.5. Slack
5.6. Snowflake
5.7. Ad Services
5.7.1. Linkedin
5.7.2. Meta
5.7.3. Google
5.7.4. Amazon