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Software Architecture Document: Metadata-Driven Workflow Engine
Version: 1.0

Date: June 12, 2025

Author: Gemini Architect

1. Motivation
The current system for executing data and machine learning pipelines involves multiple distinct workflows, each with its own dedicated codebase, repository, and CI/CD pipeline. While this approach allows for isolation, it has led to significant inefficiencies and operational challenges:

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<?xml version="1.0" encoding="UTF-8" ?>
<!DOCTYPE mapper
        PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN"
        "https://mybatis.org/dtd/mybatis-3-mapper.dtd">
<mapper namespace="com.example.mapper.OlympicWinnerMapper">

    <sql id="selectColumns">
        *
    </sql>

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# combined_pipeline.py

import multiprocessing
import time
import os
import pickle
import pandas as pd
import sys
import functools # Import functools for decorators

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def __mul__(self, other):
    if isinstance(other, (N.ndarray, list, tuple)) :
        # This promotes 1-D vectors to row vectors
        return N.dot(self, asmatrix(other))
    if isscalar(other) or not hasattr(other, '__rmul__') :
        return N.dot(self, other)
    return NotImplemented