CS-002 — Retail Sales Data Warehouse & ETL Pipeline Using Google BigQuery | DeTLeng Case Study

🚀 CS-002 — Building an Analytics-Ready Retail Dataset with BigQuery

Transforming 1.06M+ Retail Transactions into a Cloud-Based Analytics & Reporting Foundation

📊 The Challenge

Most businesses do not struggle with a lack of data.

They struggle with:

❌ Disconnected data sources

❌ Inconsistent reporting

❌ Manual spreadsheet work

❌ Unreliable KPIs

❌ Slow dashboard performance

Before any dashboard can deliver business value, the underlying data must be clean, structured, validated, and analytics-ready.

That is exactly what this project demonstrates.

🏢 About This Project

For this case study, DeTLeng worked with the famous Online Retail II Dataset, containing more than:

📈 1,067,371 Retail Transactions

covering:

  • Customer Purchases
  • Product Sales
  • Invoice Transactions
  • Revenue Information
  • Country-Level Sales
  • Multi-Year Retail Activity

Rather than jumping directly into dashboard development, the project focused on building a strong analytics foundation using Google BigQuery.

⚙️ Solution Architecture

The complete workflow followed a modern Data Engineering approach:

Excel Files
      ↓
CSV Files
      ↓
BigQuery Raw Tables
      ↓
Staging Layer
      ↓
Data Quality Validation
      ↓
Analytics Dataset
      ↓
Power BI / Looker Studio
      ↓
Business Insights

This architecture ensures scalability, reliability, and consistent reporting.

🛠️ Technologies Used

☁️ Google Cloud Platform

  • Google BigQuery
  • SQL
  • Data Warehousing
  • ETL Development

📊 Reporting & Analytics

  • Power BI
  • Looker Studio
  • Business Intelligence

🔄 Data Engineering Process

✅ Data Ingestion

Two years of retail transaction data were imported into BigQuery.

✅ Raw Data Layer

Source data was preserved without transformation to maintain data integrity.

✅ ETL Processing

Business logic was applied through SQL transformations:

  • Date Standardization
  • Revenue Calculations
  • Analytics Columns
  • Data Consolidation

✅ Data Quality Validation

The dataset was validated before KPI development.

🔍 Validation Results

Validation Check Result
Total Records 1,067,371
Date Range Dec 2009 – Dec 2011
Null Customer IDs 243,007
Negative Quantities 22,950
Negative Revenue Records 19,498

💡 Important Insight

Negative quantities and revenue values represented legitimate business activities such as:

  • Returns
  • Refunds
  • Order Adjustments
  • Cancellations

This demonstrates why understanding business context is just as important as technical validation.

📈 KPI Development

After validation, executive KPIs were developed directly within BigQuery.

KPI Summary

KPI Value
💰 Total Revenue €20.97M
📦 Total Orders 53,628
👥 Total Customers 5,942
🛒 Total Products 5,305
📊 Total Transactions 1,067,371

🌍 Business Insights

Top Revenue Countries

🥇 United Kingdom — €17.87M

🥈 EIRE — €664K

🥉 Netherlands — €554K

🏅 Germany — €431K

🏅 France — €357K

Key Finding

More than 85% of total revenue originated from the United Kingdom, highlighting strong market concentration.

🏆 Top Revenue Products

  • REGENCY CAKESTAND 3 TIER
  • Manual
  • DOTCOM POSTAGE
  • WHITE HANGING HEART T-LIGHT HOLDER
  • PAPER CRAFT, LITTLE BIRDIE

Key Finding

Home décor and gift-related products dominated revenue generation across the dataset.

📅 Revenue Seasonality

Top Performing Months:

🥇 November — €2.98M

🥈 December — €2.73M

🥉 October — €2.32M

Key Finding

Quarter 4 generated the strongest sales performance, reflecting seasonal and holiday-driven purchasing behavior.

🎯 Why This Project Matters

This project demonstrates a key principle often overlooked in Business Intelligence:

Reliable dashboards begin with reliable data.

Many organizations invest heavily in dashboard development while ignoring the quality of the underlying data.

At DeTLeng, we focus first on:

✅ Data Engineering

✅ ETL Development

✅ Data Validation

✅ Analytics Engineering

Only then do we move into reporting and visualization.

🚀 What DeTLeng Delivers

Data Engineering Solutions

✔ BigQuery Development

✔ ETL Pipelines

✔ Data Warehousing

✔ Data Cleaning & Transformation

✔ Analytics Engineering

✔ KPI Development

✔ Power BI Reporting

✔ Looker Studio Dashboards

🌐 About DeTLeng

From Raw Data to Analytics-Ready Data.

From Complexity to Clarity.

From Data Engineering to Business Value.

📧 info@detleng.com

🌍 www.detleng.com

💬 Need Help With Your Data?

If your business is struggling with:

  • Excel Chaos
  • Reporting Delays
  • Data Quality Issues
  • KPI Inconsistencies
  • BigQuery Implementation
  • Power BI Reporting

DeTLeng can help transform your data into a reliable analytics foundation.

👉 Let's Build Your Next Data Success Story.

#DataEngineering #BigQuery #ETL #SQL #PowerBI #BusinessIntelligence #AnalyticsEngineering #DataWarehouse #GoogleCloud #DeTLeng

🏆 The DeTLeng Difference

At DeTLeng, dashboards are not built as isolated reports. We engineer complete analytics solutions that combine business understanding, data engineering, transformation, modeling, analytics, visualization, and insight generation into a single delivery framework.

Every engagement is designed to help organizations move from raw data to informed business decisions faster and with greater confidence.


🤝 Need a Similar Solution?

Whether your project involves ETL pipelines, Power BI dashboards, SQL reporting, data engineering, or business analytics, DeTLeng can help transform your data into measurable business value.

🌐 Visit DeTLeng.com

Ready for Any Reporting Platform

The final analytics-ready dataset was engineered to support Power BI, Looker Studio, Excel, Tableau, and any downstream reporting platform.

One trusted data foundation. Unlimited reporting possibilities.

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