📘 Chapter 2: Exploring the theLook eCommerce Database — A Data Analyst’s First Steps

Welcome back to Performance Metrics and Insights: A Data-Driven Analysis of TheLook. In the first chapter, we introduced the theLook eCommerce dataset—a rich, realistic, synthetic dataset perfect for practicing real-world analytics. Now, in this second chapter, I’ll walk you through how I explore an unfamiliar database, just as I would on the job.

This isn’t just about writing queries—it’s about thinking like an analyst: asking the right questions, understanding business logic, and uncovering insights hidden in the schema.

🔍 Step 1: What Tables Are We Working With?

First, I need to know what’s available. A quick INFORMATION_SCHEMA query reveals the structure:

      SELECT table_name 
FROM `bigquery-public-data.thelook_ecommerce.INFORMATION_SCHEMA.TABLES`;
    

• ✅ 7 core tables—a clean, normalized schema typical of modern eCommerce platforms.

🧩 Step 2: Understand Each Table — One by One

I start with the products table—because if we don’t know what’s being sold, we can’t analyze anything.

👕 products Table

      SELECT * FROM `bigquery-public-data.thelook_ecommerce.products` LIMIT 5;
    

This shows product id, name, category, brand, retail_price, and distribution_center_id. A follow-up query reveals:

      SELECT 
  COUNT(DISTINCT id) AS num_products,
  COUNT(DISTINCT category) AS num_categories,
  COUNT(DISTINCT brand) AS num_brands
FROM `bigquery-public-data.thelook_ecommerce.products`;
    

📌 Insight: A highly diversified catalog—2,756 brands across 26 categories. This suggests a focus on variety, possibly including private labels.

🌍 distribution_centers Table

      SELECT * FROM `bigquery-public-data.thelook_ecommerce.distribution_centers`;
    

10 centers across the U.S., including major ports. This layout supports national and international shipping.

🔄 events Table — The Heart of User Behavior

      SELECT DISTINCT event_type FROM `bigquery-public-data.thelook_ecommerce.events`;
    

• 📌 Business Logic Revealed:

• Users start at home.

• Browse by department and product.

• Add to cart.

• purchase or cancel.

This is the conversion funnel in raw form.

And the uri field? It’s even more telling:

      SELECT event_type, uri 
FROM `bigquery-public-data.thelook_ecommerce.events` 
WHERE event_type = 'department' LIMIT 5;
    

• 🔍 The API is hierarchical: /department/{gender}/category/{category}/brand/{brand}. This is gold for funnel analysis and campaign tracking.

📦 inventory_items — Tracking Stock Lifecycle

      SELECT * FROM `bigquery-public-data.thelook_ecommerce.inventory_items` LIMIT 5;
    

Key fields: created_at, sold_at, cost, product_distribution_center_id.

💳 orders and order_items — The Transaction Core

      SELECT DISTINCT status FROM `bigquery-public-data.thelook_ecommerce.orders`;
    

These statuses let us measure delivery performance, return rates, and customer satisfaction.

With shipped_at and delivered_at, we can calculate average delivery time—a key KPI.

🎯 users — Who Are Our Customers?

'''sql SELECT * FROM bigquery-public-data.thelook_ecommerce.users LIMIT 5; '''

Demographics: age, gender, country, traffic_source.

      SELECT DISTINCT traffic_source FROM `bigquery-public-data.thelook_ecommerce.users`;
    

👉 Next: “Chapter 3: Data Cleaning & Preparation — Setting the Foundation for Reliable Insights”