Multi-Style Data Integration for AI/ML: 3 Use Cases

### Three key styles of data integration for AI/ML: Extract, Load, and Transform (ELT), Extract, Transform, and Load (ETL), Change Data Capture (CDC) and Streaming. These styles are combined in various ways to support diverse AI/ML projects with complex transformations, changing business conditions, and real-time requirements. The most appropriate combination depends on factors such as speed, migration complexity, and compute cost. Three example use cases illustrate the benefits of these style combinations: ELT + CDC for a customer recommendation engine, ELT + data virtualization for a diverse dataset that cannot be fully consolidated, and streaming ETL for real-time AI/ML initiatives with small data volumes and ultra-low latency windows. Each combination offers advantages in terms of speed, migration complexity, and compute cost, making them suitable for different AI/ML projects.

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