📊 End-To-End Retail Data Warehouse

A full implementation of an end-to-end retail data warehouse and analytical framework.
This repository demonstrates the complete lifecycle of data engineering and analytics —
from raw data ingestion to forecasting and executive-ready business insights, all built on validated and traceable data models.

🚀 Project Overview

An end-to-end retail data platform that transforms raw transactional data into business-ready insights through a structured data warehouse, analytics layer, and forecasting system.

🎯 Key Highlights

• End-to-end data pipeline (raw → warehouse → analytics → forecasting)
• KPI-driven business analytics framework
• Customer segmentation and LTV modeling
• Time-series forecasting for revenue and demand
• Decision-oriented dashboard design

🧰 Tech Stack

• SQL (PostgreSQL)
• Python (Pandas, Scikit-learn)
• Tableau / Power BI
• Data Modeling (Star Schema)

📊 Key Deliverables

• 📈 Interactive Dashboard (Tableau)
• 🧱 Data Warehouse (Raw → Staging → Mart)
• 🤖 Forecasting Model (Revenue Prediction)
• 📊 Business Analytics Layer (Customer, Revenue, LTV, Cohort)

⚙️ Prerequisites

Before running this project, ensure you have the following installed:

• PostgreSQL (version 12 or higher)
• Python 3.8+
• pip (Python package manager)

---

Install Python dependencies

pip install -r requirements.txt

⚡ Quick Start

1. Setup PostgreSQL

createdb retail_dw
psql -d retail_dw -f data_operations/00_admin/schema.sql

2. Run data pipeline

Execute SQL scripts in order:

data_foundation/10_raw
data_foundation/20_staging
data_modeling/
data_operations/90_tests

3. Run forecasting

See forecasting/README.md for forecasting pipeline and execution details.

For full execution details, see RUN_ORDER.md.

---

💡 Business Impact

This project enables data-driven decision-making by:

• Identifying key revenue drivers (volume vs. pricing)
• Highlighting high-value customer segments for targeted retention
• Detecting return-related operational risks
• Providing forward-looking revenue forecasts for planning

These insights directly support:

• Marketing strategy optimization
• Customer retention and lifecycle management
• Operational risk reduction
• Executive-level KPI monitoring and decision-making

---

Executive Summary

This project implements an end-to-end retail data platform that connects validated data engineering foundations to decision-oriented analytics and business recommendations.

Beyond building a warehouse, the platform emphasizes explainable analytics — revealing what drives revenue performance, customer value, product concentration, and returns impact.

Insights are translated into executive-ready recommendations through traceable analytical reasoning, bridging the gap between correct data and actionable decisions, and naturally extending into forecasting and scenario-based thinking.

---

📊 Executive Layer

📊 Executive KPI Dashboard (Power BI)

High-level KPI monitoring dashboard designed for executive decision-making.

Key Features

• Core KPIs:

  • Revenue
  • Orders
  • Customers
  • Average Order Value (AOV)

• Performance monitoring:

  • Month-over-Month (MoM) change in revenue
  • MoM change in orders

• Decision signals:

  • Rapid identification of performance drops
  • Early detection of operational risks

Business Insights

• Significant decline in both revenue and orders signals potential operational or demand-side issues
• KPI-level monitoring enables fast executive response before deeper analysis

---

🧠 Retail Performance Dashboard (Customer Segmentation)

This dashboard provides an executive-level overview of retail performance combined with customer segmentation analysis.

Key Features

• KPI monitoring:

  • Total Revenue
  • Total Orders
  • Average Order Value (AOV)

• Revenue breakdown by customer segment:

  • High Value
  • Mid Value
  • Low Value

• Behavioral analysis:

  • Order frequency vs AOV distribution
  • Segment-level purchasing patterns

• Interactive analysis:

  • Click-based filtering between segment distribution and behavior

Business Insights

• High-value customers dominate total revenue contribution
• Low-value segments show lower AOV and limited order frequency
• Clear behavioral separation between segments enables targeted strategy

---

📊 Revenue Performance Dashboard

This dashboard analyzes key revenue drivers by breaking down revenue into:

• Order volume (Orders)
• Average order value (AOV)
• Month-over-month (MoM) performance
• Drop-point detection (operational risk signals)

---

📦 Return Risk & Product Performance Dashboard

This dashboard highlights return loss, return rate, and product performance segmentation.

It identifies:

• Products driving high return losses
• High-revenue products with low return risk
• Product-level return risk patterns for decision-making

---

🔮 Forecasting & Decision Layer

This project extends beyond traditional data warehousing by incorporating a forecasting layer.

Forecasting is used to transform historical metrics into forward-looking insights:

• Predict future order volume using time-series models
• Translate forecasts into revenue scenarios (Orders × AOV)
• Support decision-making under uncertainty (downside / upside scenarios)

This bridges the gap between data infrastructure and business strategy.

---

🧱 Architecture Overview

This project follows a layered data platform architecture, governed by an explicit architecture and data lineage specification.

Raw Data → Data Foundation → Data Warehouse → Analytics → Dashboard → Forecasting → Insights

Each layer has a clear responsibility and strict separation of concerns.

Layer	Responsibility
architecture	Platform architecture, data flow, and end-to-end lineage definition
data_foundation	Data ingestion, standardization, and quality enforcement
data_modeling	Dimensional modeling (facts & dimensions, analytics marts)
data_operations	Operational validation, integrity checks, and reconciliation
business_analytics	Decision-oriented analytics and KPI decomposition
forecasting	Predictive modeling and forward-looking analysis
insights	Executive-ready insights and strategic recommendations

---

🗂️ Data Model (ERD)

---

⚙️ SQL + Python Hybrid Approach

This project combines SQL-based analytics with Python-based modeling:

• SQL is used for data preparation, KPI design, and baseline analysis
• Python is used for forecasting, evaluation, and scenario simulation

This hybrid approach ensures:

• Transparency (SQL baseline)
• Flexibility (Python modeling)
• Business alignment (decision-focused outputs)

---

Architecture & Data Lineage

See the end-to-end data pipeline and lineage diagram:

This diagram illustrates how validated data flows across layers —
from raw ingestion through staging, dimensional modeling, analytics marts, and finally BI consumption — with clear separation of concerns and traceable analytical lineage.

This architecture serves as the authoritative reference layer defining how data is validated, modeled, and consumed across the platform. All downstream layers conform to the data flow and contracts defined here.

---

📁 Layer Descriptions

0. Architecture

Defines the platform-level architecture and end-to-end data lineage, serving as the authoritative reference for how data flows across layers.

• Data pipeline & lineage diagram
• Layer responsibilities and system flow documentation

Path: architecture/
Docs: architecture/README.md

1. Data Foundation

Builds a trusted base layer for all downstream analytics.

• Raw data ingestion
• Standardization and cleansing
• Foundational data quality checks

Path: data_foundation/
Docs: data_foundation/README.md

---

2. Data Modeling

Creates analytics-ready dimensional models optimized for BI and analysis.

• Star schema design
• Fact and dimension table creation
• KPI-oriented analytics marts

Path: data_modeling/
Docs: data_modeling/README.md

---

3. Data Operations

Ensures platform reliability, correctness, and reproducibility.

data_operations/00_admin

• Schema creation
• Extension setup
• Idempotent environment initialization

data_operations/90_tests

• Referential integrity checks
• Fact-level sanity validation
• Cross-layer reconciliation

Path: data_operations/
Docs: data_operations/README.md

---

4. Business Analytics

This layer contains fully implemented, decision-oriented analytical modules, progressing from performance explanation to risk identification and executive-level recommendations.

Implemented modules include:

• Revenue Driver Analysis
• Customer Segmentation
• Product Mix Analysis
• Returns Analysis
• Customer Lifetime Value (LTV)
• Revenue Driver × Segment Analysis
• Price Sensitivity (Discount Proxy) Analysis
• Cohort Retention Analysis
• Operational Risk Analysis
• Data Quality & Assumption Disclosure
• Metric Layer (KPI Mart)

Each module contains:

• SQL logic
• Execution result screenshots
• Business interpretation and implications

Path: business_analytics/
Docs: business_analytics/README.md

---

5. Forecasting

This layer extends validated historical analytics into forward-looking predictions using a structured end-to-end SQL + Python pipeline.

Key capabilities include:

• Time-series forecasting of order volume
• Revenue projection using Orders × AOV decomposition
• Model evaluation (MAPE, error tracking)
• Scenario analysis (baseline, upside, downside)

The forecasting system is built on top of:

• SQL-based feature engineering
• ML-ready dataset preparation
• Data validation and KPI consistency

This ensures that predictions are:

• Reproducible
• Consistent with historical analytics
• Directly applicable to business decision-making

Path: forecasting/
Docs: forecasting/README.md

---

6. Insights

This layer represents the executive-facing decision output of the analytics stack.

Rather than existing as a separate module, insights are embedded within the Business Analytics layer through:

• Executive summaries
• Derived recommendations in each analytical module
• A centralized Business Recommendation Layer

All insights are directly traceable to validated analytical findings, ensuring transparency, explainability, and decision-level clarity.

---

🧠 Core Design Principles

• Layered responsibility and separation of concerns
• Validation before insight
• Explainable and auditable analytics
• Business-first thinking

Clean data enables trust.
Trust enables decisions.

---

🚦 How to Run the Project

1. Environment setup
   Run scripts in data_operations/00_admin

2. Data ingestion & cleaning
   Execute data_foundation/10_raw → data_foundation/20_staging

3. Data modeling
   Build dimensional models in data_modeling/

4. Validation
   Run checks in data_operations/90_tests

5. Analytics
   Explore modules in business_analytics/

---

📌 Visual Evidence

Each layer includes result/ folders containing:

• SQL execution screenshots
• Validation outputs
• Analytical results

This makes the project auditable, traceable, and reproducible.

---

📈 Why This Project Matters

This repository demonstrates:

• End-to-end data platform architecture design
• Strong SQL and dimensional modeling discipline
• Data quality–first engineering mindset
• Decision-oriented analytics aligned with business impact
• A reproducible, enterprise-style analytics framework

---

🗂️ Repository Structure

End-To-End-Retail-Data-Warehouse/
│
├── architecture/       # Data warehouse architecture and lineage design
│ ├── lineage_pipeline_diagram.png
│ └── README.md
│
├── data_foundation/    # Data ingestion, staging, and base transformations
│ ├── result/
│ ├── sql/
│ └── README.md
│
├── data_modeling/      # Star schema modeling (fact & dimension tables)
│ ├── erd/
│ │ └── dw_core_erd.png
│ ├── result/
│ ├── sql/
│ └── README.md
│
├── data_operations/    # ETL/ELT pipelines and data processing workflows
│ ├── result/
│ ├── sql/
│ └── README.md
│
├── business_analytics/ # KPI dashboards, driver analysis, and business insights
│ ├── 00_dashboard/                                  # Executive KPI dashboards
│ ├── 00_data_mart/                                  # Aggregated KPI data layer
│ ├── 01_revenue_driver_analysis/                    # Revenue decomposition (Orders × AOV)
│ ├── 02_customer_segmentation/                      # Customer segmentation and behavior analysis
│ ├── 03_product_mix_analysis/                       # Product performance and category trends
│ ├── 04_returns_analysis/                           # Return rate and operational impact
│ ├── 05_ltv_analysis/                               # Customer lifetime value modeling
│ ├── 06_revenue_driver_x_segment/                   # Revenue drivers across customer segments
│ ├── 07_price_sensitivity_discount_proxy_analysis/  # Price elasticity and discount impact
│ ├── 08_cohort_retention/                           # Retention and cohort behavior analysis
│ ├── 09_operational_risk_analysis/                  # Risk signals and operational anomalies
│ ├── 10_data_quality_assumptions/                   # Data validation and assumptions tracking
│ ├── 11_metric_layer/                               # KPI definitions and metric standardization
│ └── README.md
│
├── forecasting/        # Forecasting models and predictive analytics
│ ├── data/
│ ├── notebooks/
│ ├── sql/
│ ├── result/
│ └── README.md
│
├── research/           # Research papers, workshop submissions, and publications
│
├── requirements.txt    # Python dependencies and environment setup
│
├── RUN_ORDER.md        # Execution sequence for end-to-end pipeline
│
└── README.md