Purpose of the Data Integration Guide
The C3 AI Data Integration Guide explains how external data is integrated, transformed, validated, and made available for use in applications and AI workflows on the C3 Agentic AI Platform.
Most data used by applications originates outside the C3 Agentic AI Platform and must be connected, shaped, and operationalized before it can be reliably consumed. This guide focuses on the patterns, techniques, and workflows used to perform that work, from connecting to source systems through monitoring ongoing data loads.
The guide introduces Data Fusion as a core technique for implementing data integration through low-code workflows. Data Fusion provides a structured way to configure data sources, mappings, validation rules, and ingestion jobs in C3 AI Studio.
Data Fusion is in Beta. Please contact your C3 AI representative to enable this feature.
Purpose of this guide
This guide helps you understand how data flows from external systems into usable application data on the C3 Agentic AI Platform, including how to:
- Connect external systems using Connect to Source Systems.
- Understand integration patterns such as ETL, streaming, and batch processing through Data Integration Techniques.
- Configure ingestion, mapping, validation, and CDC workflows using Data Fusion, starting with Select and Modify Data Sources in Data Fusion.
- Implement programmatic ingestion and virtualization using Data Integration Pipelines Overview.
- Ingest and process unstructured data using Understand Unstructured Data Integration (UDI) Pipelines.
- Observe and troubleshoot ingestion jobs using Monitor Data Loads using Console.
You can approach these topics in any order, though most workflows begin by connecting a source system and defining how incoming data should be transformed and validated.