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dataproc Framework: Implementation Strategy

This document outlines the verified approach for implementing industrial data pipelines using the dataproc-engine framework.

1. Modular Provider Framework

The system utilizes a Provider Pattern. Adding a new industry involves: 1. Defining the industry-specific schema (e.g., Finance). 2. Implementing a BaseProvider subclass with extract(), transform(), and validate() logic. 3. Registering the provider in the DatasetEngine.

2. Hardened Core Components

  • DatasetEngine: Manages the unified async pipeline and provider registry.
  • LLMManager: Implements a 3-tier cognitive fallback (Cloud -> Ollama -> Heuristic) with 100% fail-safe extraction.
  • DataCorrelator: Establishes cross-industry signals (e.g., linking Enterprise Revenue to Energy Reliability).
  • Safety Layer: Automated rotational backups and PII scrubbing.

3. Industrial Stabilization (100% Parity)

The following 16 sectors have been fully hardened and verified against industrial-grade schemas: - Finance (Finance Sector Guide) - Healthcare (Healthcare Sector Guide) - Energy (Energy Sector Guide) - Telecom (Telecom Sector Guide) - E-Commerce (Ecommerce Sector Guide) - Agriculture (Agriculture Sector Guide) - Transportation (Transportation Sector Guide) - Manufacturing (Manufacturing Sector Guide) - Demographics (Demographics Guide) - Labor (Labor Guide) - Environment (Environment Guide) - Education (Education Guide) - Housing (Housing Guide) - Media & Entertainment (Media Guide) - Decision Support (Decision Support Guide) - Unstructured (Unstructured Guide)

4. Verification & Hardening

  • Mission-Critical Testing: 90%+ total coverage required for every core module.
  • Zero-Bundling Policy: All restricted data (Clinical Database V2, Ookla) is handled via high-fidelity dynamic simulation.