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genpark-enterprise-data-lineage-impact-graph-analyzer-skill

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SUMMARY

Enterprise Data Lineage & Schema Change Blast-Radius Impact Analyzer. Constructs column- and table-level Directed Acyclic Graphs (DAGs) across Tencent Cloud Data Warehouses, Tencent Docs spreadsheets, and BI executive dashboards, performs u | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-enterprise-data-lineage-impact-graph-analyzer-skill

GenPark AI
LinkedIn
License: MIT
Dependencies
MCP Compliant

Enterprise Data Lineage & Schema Change Blast-Radius Impact Analyzer. Constructs column- and table-level Directed Acyclic Graphs (DAGs) across Tencent Cloud Data Warehouses, Tencent Docs spreadsheets, and BI executive dashboards, performs upstream provenance and downstream blast-radius traversal, and renders Mermaid lineage diagrams.

🌐 Official GenPark AI Ecosystem Links

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

"""Example usage for EnterpriseDataLineageImpactGraphAnalyzer."""
import sys
import json
from client import EnterpriseDataLineageImpactGraphAnalyzer

sys.stdout.reconfigure(encoding='utf-8')

def main():
    print("=== Enterprise WorkBuddy Data Lineage & Schema Blast-Radius Analyzer Demo ===")
    analyzer = EnterpriseDataLineageImpactGraphAnalyzer()

    pipeline_edges = [
        {"source": "wechat_pay_raw_stream", "target": "dwd_merchant_settlement", "transformation": "FLINK_STREAM", "criticality": "HIGH"},
        {"source": "dwd_merchant_settlement", "target": "ads_q4_gmv_summary", "transformation": "SPARK_SQL", "criticality": "CRITICAL"},
        {"source": "ads_q4_gmv_summary", "target": "tencent_docs_board_sheet", "transformation": "MCP_CONNECTOR", "criticality": "CRITICAL"},
        {"source": "tencent_docs_board_sheet", "target": "workbuddy_ceo_briefing_ppt", "transformation": "SLIDE_COMPILER", "criticality": "CRITICAL"}
    ]

    # 1. Register DAG lineage edges
    print("\n--- 1. Registering Enterprise Data Pipeline Lineage Graph ---")
    reg = analyzer.register_lineage_edges(pipeline_edges)
    print(f"Registered {reg['edges_registered']} edges across {reg['total_nodes']} nodes.")

    # 2. Perform Downstream Blast-Radius Impact Analysis
    print("\n--- 2. Analyzing Downstream Impact of Schema Change on 'wechat_pay_raw_stream' ---")
    impact = analyzer.analyze_downstream_impact("wechat_pay_raw_stream")
    print(f"Blast Radius Severity: {impact['blast_radius_severity']} (Impacted Artifacts: {impact['total_downstream_impacted']})")
    for dep in impact["impacted_dependents"]:
        print(f"  Hop {dep['hop_distance']}: {dep['node_id']} (via {dep['transformation']}, Criticality: {dep['criticality']})")

    # 3. Render Mermaid Architecture Diagram
    print("\n--- 3. Rendering Mermaid Lineage Diagram ---")
    print(analyzer.render_mermaid_lineage())

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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