Skip to content

Generic ETL Framework Architecture

Overview

The ETL framework is designed to be modular, extensible, and robust. It uses a Provider Pattern to abstract industry-specific data source logic from the core processing engine.

Core Components

1. Dataset Engine (Orchestrator)

The central controller that: * Loads industry configurations from a Unified 16-Sector Registry. * Instantiates the appropriate Provider lifecycle. * Handles parallel execution and resource management (e.g., rate limiting).

2. Provider Lifecycle

Each industry pilot implements a Provider that follows a standardized 4-stage lifecycle:

  1. Extract: Fetches raw data from external APIs or files. Supports PDF/Web/REST.
  2. Transform (Async): Maps raw data into StandardSchema using Tiered LLM extraction (Cloud -> Local -> Heuristic).
  3. Validate: Performs integrity checks and domain-specific consistency verification.
  4. Correlate: Establishes cross-industry links and enriches records with fuzzy-matched identity resolution.

Deep Hardening Layers

🛡️ 1. Resiliency & Fault Tolerance

The BaseProvider implements a mission-critical resiliency layer: * Circuit Breaker: Detects repeated failures (e.g., API 500s or 429s) and trips to prevent cascading exhaustion of resources. * Exponential Backoff: Automatic retries with randomized jitter to handle transient network instability gracefully.

🔐 2. Security Layer

To ensure enterprise-grade compliance during industrial signal extraction: * Autonomous PII Scrubbing: A regex-based engine in the BaseProvider cleans emails, phone numbers, and sensitive identifiers from raw unstructured text before it reaches the LLM inference tier. * Immutable Checksums: All records are now secured with a SHA-256 hash of their content, ensuring data-aware integrity throughout the pipeline.

Data Flow Diagram

graph TD
    A[Dataset Engine] -->|Register| D{Provider Instance}
    D --> Resiliency[Circuit Breaker/Retry]
    Resiliency --> E[Async Extractor]
    D --> Security[PII Scrubber]
    Security --> F[Async Transformer]
    F -->|Tiered Fallback| LLM[LLMManager]
    D --> G[Validator]
    D --> L[DataCorrelator]
    L -->|Fuzzy Match| identity[Identity Resolution]
    G --> J[(Final Dataset)]

Extensibility Pattern

To add a new industry (e.g., "Healthcare"): 1. Implement HealthcareProvider (overriding extract() and transform()). 2. Register the provider in dataproc_engine/cli/main.py. 3. The DatasetEngine and DataCorrelator automatically handle the orchestration and cross-vertical matching.