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E-commerce Domain

This document describes the E-commerce data domain, including data sources, processing pipelines, and analytical models for customer behavior and product analytics.

Overview

The E-commerce domain focuses on customer and product data, providing insights into customer behavior, product performance, order analytics, and business metrics. This domain demonstrates how to handle transactional e-commerce data in a modern data platform.

Data Sources

Mock E-commerce Data

Primary Source: Generated mock data - Data Format: CSV, JSON - Update Frequency: Daily - Data Volume: ~5K orders per day - Schema: Customer details, product information, order data

Data Schema

json { "customer_id": "string", "customer_name": "string", "email": "string", "phone": "string", "address": "string", "city": "string", "state": "string", "zip_code": "string", "customer_segment": "string", "registration_date": "date", "order_id": "string", "order_date": "timestamp", "product_id": "string", "product_name": "string", "category": "string", "subcategory": "string", "brand": "string", "price": "decimal", "quantity": "integer", "discount": "decimal", "total_amount": "decimal", "payment_method": "string", "shipping_method": "string", "order_status": "string" }

Data Pipeline

Bronze Layer (Raw Data)

Storage: MinIO datalake/bronze/ecommerce/ Format: CSV, JSON Partitioning: By date (year=YYYY/month=MM/day=DD)

python @asset def ecommerce_bronze(): """Ingest raw e-commerce data from mock sources""" # Mock data generation # Schema validation # Data quality checks pass

Silver Layer (Prepared Data)

Storage: MinIO datalake/silver/ecommerce/ Format: Delta Lake Processing: Apache Spark

python @asset(deps=[ecommerce_bronze]) def ecommerce_silver(): """Clean and prepare e-commerce data""" # Data cleaning # Schema standardization # Duplicate removal # Data type conversion # Customer segmentation pass

Gold Layer (Analytics Data)

Storage: MinIO warehouse/ecommerce/ Format: Apache Iceberg Processing: dbt transformations

```sql -- Customers table CREATE TABLE customers ( customer_id STRING, customer_name STRING, email STRING, phone STRING, address STRING, city STRING, state STRING, zip_code STRING, customer_segment STRING, registration_date DATE, created_at TIMESTAMP, updated_at TIMESTAMP ) USING ICEBERG;

-- Products table CREATE TABLE products ( product_id STRING, product_name STRING, category STRING, subcategory STRING, brand STRING, price DECIMAL(10,2), created_at TIMESTAMP, updated_at TIMESTAMP ) USING ICEBERG;

-- Orders table CREATE TABLE orders ( order_id STRING, customer_id STRING, order_date TIMESTAMP, total_amount DECIMAL(10,2), payment_method STRING, shipping_method STRING, order_status STRING, created_at TIMESTAMP, updated_at TIMESTAMP ) USING ICEBERG PARTITIONED BY (order_date);

-- Order items table CREATE TABLE order_items ( order_id STRING, product_id STRING, quantity INT, price DECIMAL(10,2), discount DECIMAL(10,2), total_amount DECIMAL(10,2), created_at TIMESTAMP, updated_at TIMESTAMP ) USING ICEBERG; ```

Analytical Models

Customer Analytics Model

Purpose: Customer behavior and segmentation Key Metrics: - Customer lifetime value - Purchase frequency - Customer segments - Retention rates

Product Performance Model

Purpose: Product sales and performance analysis Key Metrics: - Sales volume - Revenue contribution - Product categories - Brand performance

Order Analytics Model

Purpose: Order processing and fulfillment Key Metrics: - Order volume - Average order value - Payment methods - Shipping performance

Business Metrics Model

Purpose: Overall business performance Key Metrics: - Revenue trends - Growth rates - Market share - Operational efficiency

Data Quality

Validation Rules

```python

Data quality checks

def validate_ecommerce_data(df): """Validate e-commerce data quality"""

# Check required fields
required_fields = ['customer_id', 'order_id', 'product_id', 'order_date']
for field in required_fields:
    assert df[field].isnull().sum() == 0, f"Missing values in {field}"

# Check data types
assert df['order_date'].dtype == 'timestamp', "Invalid order date format"
assert df['price'].dtype == 'decimal', "Invalid price format"
assert df['quantity'].dtype == 'integer', "Invalid quantity format"

# Check value ranges
assert df['price'].min() > 0, "Invalid price range"
assert df['quantity'].min() > 0, "Invalid quantity range"
assert df['total_amount'].min() >= 0, "Invalid total amount range"

# Check logical constraints
assert (df['total_amount'] == df['price'] * df['quantity'] - df['discount']).all(), "Invalid total calculation"

return True

```

Data Quality Metrics

  • Completeness: Percentage of non-null values
  • Accuracy: Data validation against source
  • Consistency: Cross-field validation
  • Timeliness: Data freshness tracking

Query Examples

Basic Queries

```sql -- Top 10 customers by total spending SELECT c.customer_id, c.customer_name, c.customer_segment, COUNT(o.order_id) as order_count, SUM(o.total_amount) as total_spent FROM customers c JOIN orders o ON c.customer_id = o.customer_id WHERE o.order_date >= CURRENT_DATE - INTERVAL '30' DAY GROUP BY c.customer_id, c.customer_name, c.customer_segment ORDER BY total_spent DESC LIMIT 10;

-- Product performance by category SELECT p.category, p.subcategory, COUNT(DISTINCT oi.product_id) as product_count, SUM(oi.quantity) as total_quantity, SUM(oi.total_amount) as total_revenue, AVG(oi.price) as avg_price FROM products p JOIN order_items oi ON p.product_id = oi.product_id JOIN orders o ON oi.order_id = o.order_id WHERE o.order_date >= CURRENT_DATE - INTERVAL '30' DAY GROUP BY p.category, p.subcategory ORDER BY total_revenue DESC; ```

Advanced Analytics

sql -- Customer lifetime value analysis WITH customer_metrics AS ( SELECT c.customer_id, c.customer_name, c.customer_segment, c.registration_date, COUNT(DISTINCT o.order_id) as total_orders, SUM(o.total_amount) as total_spent, MIN(o.order_date) as first_order_date, MAX(o.order_date) as last_order_date, DATEDIFF(MAX(o.order_date), MIN(o.order_date)) as customer_lifespan_days FROM customers c JOIN orders o ON c.customer_id = o.customer_id GROUP BY c.customer_id, c.customer_name, c.customer_segment, c.registration_date ), customer_segments AS ( SELECT customer_segment, COUNT(*) as customer_count, AVG(total_orders) as avg_orders, AVG(total_spent) as avg_spent, AVG(customer_lifespan_days) as avg_lifespan_days, AVG(total_spent / customer_lifespan_days) as daily_value FROM customer_metrics GROUP BY customer_segment ) SELECT customer_segment, customer_count, ROUND(avg_orders, 2) as avg_orders, ROUND(avg_spent, 2) as avg_spent, ROUND(avg_lifespan_days, 2) as avg_lifespan_days, ROUND(daily_value, 2) as daily_value FROM customer_segments ORDER BY avg_spent DESC;

Performance Optimization

Partitioning Strategy

```sql -- Partition by order date for time-based queries PARTITIONED BY (order_date)

-- Additional partitioning for customer queries PARTITIONED BY (order_date, customer_segment) ```

Indexing

sql -- Create indexes for common query patterns CREATE INDEX idx_orders_customer ON orders (customer_id); CREATE INDEX idx_orders_date ON orders (order_date); CREATE INDEX idx_order_items_product ON order_items (product_id); CREATE INDEX idx_customers_segment ON customers (customer_segment);

Caching

  • Query Result Caching: Cache frequent query results
  • Data Caching: Cache frequently accessed data
  • Metadata Caching: Cache table metadata

Monitoring

Key Metrics

  • Data Volume: Orders processed per day
  • Data Quality: Quality score trends
  • Processing Time: Pipeline execution duration
  • Query Performance: Average query response time

Alerts

  • Data Quality: Quality score below threshold
  • Processing Delays: Pipeline execution time exceeded
  • Data Volume: Unusual data volume changes
  • Query Performance: Slow query detection

Business Use Cases

E-commerce Analytics

  • Customer Analysis: Behavior patterns and segmentation
  • Product Performance: Sales and inventory analysis
  • Order Management: Processing and fulfillment
  • Revenue Analysis: Financial performance tracking

Customer Insights

  • Purchase Patterns: Buying behavior analysis
  • Customer Segmentation: Demographic and behavioral segments
  • Retention Analysis: Customer loyalty metrics
  • Personalization: Recommendation engine data

Data Governance

Access Control

  • Role-based Access: Different access levels for different users
  • Data Classification: Sensitive data identification
  • Audit Logging: All data access logged
  • Compliance: GDPR and privacy regulations

Data Lineage

  • Source Tracking: Data origin documentation
  • Transformation Logging: All data transformations logged
  • Quality Tracking: Data quality metrics over time
  • Impact Analysis: Change impact assessment

Future Enhancements

Planned Features

  • Real-time Analytics: Live customer behavior tracking
  • ML Models: Recommendation and prediction models
  • Personalization: Dynamic content and offers
  • A/B Testing: Experimentation framework

Integration Opportunities

  • External Data: Market data, competitor analysis
  • ML Pipeline: Machine learning model integration
  • API Development: Real-time data access APIs
  • Dashboard Enhancement: Advanced visualization features
Last update: October 3, 2025
Created: October 3, 2025