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FFG Data Pipeline Documentation

Welcome to the Fuel for Growth Data Pipeline

The FFG (Fuel for Growth) Data Pipeline is an enterprise-grade data processing system designed to transform and consolidate financial data across multiple business dimensions for Coty's luxury division.

Overview

The FFG Pipeline processes three main data streams:

  • Sell-Out Data: Retail sales data from partners
  • Sell-In Data: Internal sales data from SAP systems
  • RSP Data: Recommended Selling Prices and floor prices

These data streams are processed, enriched, ventilated, and consolidated to provide comprehensive business insights and support price corridor analysis.

Key Features

  • Multi-Source Integration: Combines data from Azure Blob Storage, SQL Data Warehouse, and MDM systems
  • Advanced Ventilation Logic: Allocates financial metrics across house and product levels
  • Intelligent Deduplication: Smart append mode prevents data duplication
  • Price Corridor Analysis: Identifies pricing opportunities and risks
  • Automated Processing: End-to-end automation with Power Automate integration

Quick Start

Prerequisites

  • Databricks workspace with SQL Warehouse access
  • Azure Storage account with configured containers
  • Service principal authentication configured
  • Python 3.8+ environment

Basic Usage

from main import run_pipeline

# Execute the complete pipeline
success = run_pipeline()

Architecture

The pipeline follows a medallion architecture:

Bronze Layer → Silver Processing → Gold Layer → Analytics

Processing Steps

  1. Data Ingestion: Load source files from Azure Blob Storage
  2. Mapping Application: Enrich data with business mappings
  3. MDM Enrichment: Add master data attributes
  4. Ventilation: Allocate metrics across dimensions
  5. Consolidation: Combine all data streams
  6. Corridor Analysis: Calculate price corridors and opportunities

Support

For questions or issues: - Slack: #ffg-pipeline - Email: data-engineering@coty.com - GitHub: coty/ffg-pipeline


Last updated: September 2025