Overview of Structured Data Integration (SDI) Pipeline

The topics here introduce the end‑to‑end workflow for building a Structured Data Integration (DI) pipeline using the Data Integration canvas in the Data Fusion experience. A Structured DI Pipeline provides a guided process for ingesting structured data from external systems, shaping it through transformations, and loading the resulting records into canonical or entity types within the application model.

The topics in this guide walk through each stage of the configuration flow. You begin by connecting to a source system and defining a source collection that identifies the dataset to ingest. You then create or reuse a source schema, configure transforms to shape the data, and map source fields to target fields. After defining the pipeline, you configure runtime parameters, run the ingestion, and validate the results using execution monitoring tools.

This workflow represents the standard (“happy path”) pattern for structured data ingestion. While details may vary depending on the connector or source system, the overall pipeline structure remains consistent across Data Fusion. The contents focus on the core configuration process and does not include advanced scenarios or connector‑specific variations. For foundational concepts about the Data Fusion experience, the Data Integration canvas, or pipeline pattern options, see the related conceptual topics.