Pricing Insights: Analyzing E-commerce Price Trends & Dynamic Pricing

Introduction & The Problem

In the fast-paced world of e-commerce, understanding price trends and optimizing dynamic pricing strategies is essential for maximizing revenue and staying competitive. But how do you build a robust analytics pipeline that can ingest, clean, and analyze millions of product records from platforms like eBay and Kaggle, and deliver actionable insights to business stakeholders?

The ecommerce-pricing-insights project tackles this challenge head-on. It provides a production-ready workflow for data engineering, warehousing, and business intelligence, enabling teams to:

Ingest and validate large-scale e-commerce datasets
Design and build star-schema data warehouses
Analyze price trends, category leaders, and brand performance
Implement and test dynamic pricing models
Visualize insights with interactive dashboards

Project Structure & Modular Architecture

The project is organized for clarity, scalability, and maintainability, following best practices in data engineering and analytics:

ecommerce-pricing-insights/
├── data/
│   ├── raw/                      # Raw eBay/Kaggle datasets
│   └── processed/                # Cleaned and feature-enriched data
├── notebooks/
│   ├── apache_spark.ipynb        # Scalable data processing
│   ├── Data_check.ipynb          # Data validation and exploration
│   └── Data_cleaning_code.ipynb  # Data cleaning workflows
├── sql/
│   ├── ingestion/                # Data ingestion scripts
│   ├── cleaning/                 # Data quality and validation
│   ├── schema/                   # Warehouse schema design
│   ├── performance/              # Query optimization
│   ├── procedures/               # Stored procedures
│   └── dynamic_pricing/          # Pricing models and analytics
├── dashboard/
│   └── E-commerce-dashboard.py  # Plotly dashboard
├── README.md                     # Project documentation

Advantages of This Format

Separation of Concerns

SQL scripts, notebooks, and dashboards are organized by workflow stage, making it easy to maintain and extend.

Testability

Each module can be validated independently, and notebooks provide reproducible data checks and cleaning steps.

Reusability

Core SQL queries and cleaning routines can be reused across different datasets and reporting needs.

Scalability

The architecture supports scaling from small test datasets to millions of records using Spark and optimized SQL.

Tools & Technologies Used

Python 3.x

Jupyter notebooks for data cleaning, validation, and scalable processing with Apache Spark.

SQL

Comprehensive SQL scripts for ingestion, cleaning, schema design, performance tuning, and analytics.

Plotly

Interactive dashboards for visualizing price trends, category leaders, and dynamic pricing insights.

Pytest

Automated testing for data validation and workflow integrity.

SQLFluff

SQL linting and formatting for code quality and consistency.

Key Analytics & Dynamic Pricing Models

The project implements several advanced analytics and pricing models:

Price Trend Analysis: Track average, median, and delta prices across brands and categories.
Category & Brand Leaders: Identify top-performing brands and categories using summary tables and materialized views.
Dynamic Pricing Models: Compare platform pricing and suggest optimal price points for products.
Performance Tuning: Optimize queries for speed and scalability with indexes and query plans.
Data Validation: Ensure data integrity with validation scripts and referential checks.

Testing & Code Quality

Quality assurance is built into the workflow through automated testing and code linting:

Pytest for validating data and workflow outputs
SQLFluff for SQL code style and error checking
Jupyter notebooks for reproducible data cleaning and validation

Usage Example

Typical workflow for running the analytics pipeline:

# Run data cleaning and validation in notebooks
jupyter notebook notebooks/Data_cleaning_code.ipynb
jupyter notebook notebooks/Data_check.ipynb

# Execute SQL scripts for ingestion and analytics
psql -f sql/ingestion/create_tables_2.sql
psql -f sql/ingestion/ingest_data_1.sql
psql -f sql/dynamic_pricing/dynamic_pricing_model.sql

# Visualize results in Dash
Open dashboard/E-commerce-dashboard.py

Data Generation & Testing

The project includes synthetic data and test scripts to validate the pipeline and demonstrate analytics capabilities.

Raw and processed datasets for realistic e-commerce scenarios
Automated tests for SQL and Python workflows
Reproducible results for business intelligence reporting

Key Learnings & Best Practices

Architecture Decisions

Organize code by workflow stage for clarity
Use SQL linting and automated tests for quality
Leverage notebooks for exploration and reproducibility
Design scalable schemas for analytics

Development Workflow

Automate repetitive tasks with Makefile or scripts
Combine linting, formatting, and tests in CI pipeline
Maintain both notebooks and production SQL code
Write integration tests for workflow validation

Future Enhancements

Potential extensions to explore:

Real-time price monitoring: Integrate streaming data sources for live analytics
Machine learning models: Predict optimal prices and demand using ML algorithms
Automated reporting: Schedule dashboard updates and report generation
Advanced visualizations: Interactive charts for price trends and category analysis

Conclusion

The ecommerce-pricing-insights project demonstrates how to build a scalable, production-ready analytics pipeline for e-commerce price trends and dynamic pricing. By following best practices in data engineering, analytics, and business intelligence, the project delivers actionable insights and a foundation for future enhancements.

Whether you're optimizing prices, analyzing market trends, or building BI dashboards, this project provides a real-world example of professional analytics development.

View the Full Project

Explore the complete source code, documentation, and examples on GitHub:

GitHub Repository

Next up: Integrating machine learning for price prediction and real-time analytics! Stay tuned!