🧮 Data Driven Integration

Data-Driven patterns focus on the movement and transformation of data itself, often in batch or incremental modes. These are ideal for analytical and reporting use cases.

🧠 Key Characteristics

• Data is the trigger for integration
• Batch or incremental processing
• Optimized for large volumes
• ETL/ELT, CDC, file-based ingestion

🧮 Differential Data Synchronization

Regular data synchronization using change data capture (CDC). Optimized for large volumes.

Example
Daily synchronization of sales data from Salesforce to our Data Warehouse to feed Power BI dashboards.

📦 Full Data Synchronization

Full table or file loads at scheduled intervals. Used for low-volume sources or when CDC is not available.

Example
Weekly loading of Excel forecast files sent by subsidiaries into the Azure Data Lake.

✅ When to Use

• Data is updated periodically
• Historical or analytical processing is needed
• Source systems do not support real-time APIs