While Platform transforms handle core ETL operations, Data Integrator transforms are designed for advanced scenarios, specifically data warehouse loading and complex data structure manipulation.
Watch the following video to learn more about Data Integrator transforms.
The following table summarizes the key differences between Platform and Integrator transforms:
Integrator Transforms vs. Platform Transforms
| Dimension | Platform Transforms | Integrator Transforms |
|---|---|---|
| Primary Purpose | General-purpose data movement and transformation. | Specialized patterns for data warehouse loading and change management. |
| Typical Use Cases | Filtering rows, applying business rules, validating data quality, and merging streams. | Tracking data changes, generating surrogate keys, managing historical records, and processing CDC feeds. |
| Configuration Approach | SQL-like mapping rules with a straightforward setup. | Metadata-driven, requiring an understanding of data warehouse design patterns. |
| Best Suited For | Any ETL scenario requiring data manipulation, regardless of the target system. | Data warehouse loading workflows where change detection and history are critical. |
Summary
In this lesson, you learned that Data Integrator transforms:
- Extend the capabilities of Platform transforms for advanced tasks like data warehouse loading and structural manipulation.
- Support data warehouse patterns, including change detection, historical tracking, surrogate key generation, and time dimension population.
- Enable structural transformations such as flattening hierarchies, pivoting data, processing XML, and reshaping rows into columns.
- Optimize performance across heterogeneous sources by pushing operations to database servers and managing large datasets efficiently.