haipipe-data-0-overview

The haipipe data pipeline architecture consists of six layers: Source, Record, Case, AIData, Model, and Endpoint. Each layer handles specific roles and data flow responsibilities to support onboarding, auditing, and framework extension.

To map a dataset to the haipipe data pipeline, follow the architectural overview to identify layer responsibilities, map the dataset to the appropriate layer, and identify required Fns to evaluate where to customize the workflow.

Yes, the haipipe data pipeline framework supports custom workflow extension. It provides design principles, builder patterns, and discovery mechanisms that allow software engineers and data scientists to extend the workflow.

The best way to onboard to the haipipe framework is by reviewing the comprehensive architecture map. It explains the 6-layer pipeline rationale, layer mapping, design metaphors, prerequisites, and discovery commands.

The haipipe architecture separates data processing into distinct layers to centralize navigation and reference. This design choice helps teams quickly understand specific layer responsibilities, audit data flow, and support framework extension.