Saved in:
| Main Author: | |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2025
|
| Online Access: | https://doi.org/10.5281/zenodo.17466536 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866902194677088256 |
|---|---|
| author | Kadavala, Priyanka Raju |
| author_facet | Kadavala, Priyanka Raju |
| contents | <h2> Project Overview</h2> <p dir="auto">This repository contains the full implementation and documentation of my Master’s thesis,<br><strong>“Design and Implementation of an End-to-End Data Engineering Pipeline for E-Commerce Analytics.”</strong></p> <p dir="auto">The project demonstrates how a <strong>lightweight, automated data engineering pipeline</strong> can transform raw e-commerce data into actionable business insights.<br>The research emphasizes <strong>cost-effective, scalable analytics</strong> using <strong>open-source tools</strong> — making advanced data-driven decision-making accessible for small and medium-sized enterprises (SMEs).</p> <div dir="auto"> <h2> Research Findings</h2> <a href="https://github.com/Priyanka-KP-DA/ecommerce-data-pipeline#-research-findings"></a></div> <p dir="auto">The developed pipeline successfully automated the extraction, cleaning, and transformation of the data, reducing manual preparation time by approximately <strong>85%</strong>.<br>Through Power BI visualization, it enabled insights into <strong>sales trends</strong>, <strong>customer segmentation</strong>, and <strong>product performance</strong>, demonstrating that even small e-commerce firms can achieve data-driven intelligence using open-source tools.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17466536 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | End-to-End Data Engineering Pipeline for E-Commerce Analytics Kadavala, Priyanka Raju <h2> Project Overview</h2> <p dir="auto">This repository contains the full implementation and documentation of my Master’s thesis,<br><strong>“Design and Implementation of an End-to-End Data Engineering Pipeline for E-Commerce Analytics.”</strong></p> <p dir="auto">The project demonstrates how a <strong>lightweight, automated data engineering pipeline</strong> can transform raw e-commerce data into actionable business insights.<br>The research emphasizes <strong>cost-effective, scalable analytics</strong> using <strong>open-source tools</strong> — making advanced data-driven decision-making accessible for small and medium-sized enterprises (SMEs).</p> <div dir="auto"> <h2> Research Findings</h2> <a href="https://github.com/Priyanka-KP-DA/ecommerce-data-pipeline#-research-findings"></a></div> <p dir="auto">The developed pipeline successfully automated the extraction, cleaning, and transformation of the data, reducing manual preparation time by approximately <strong>85%</strong>.<br>Through Power BI visualization, it enabled insights into <strong>sales trends</strong>, <strong>customer segmentation</strong>, and <strong>product performance</strong>, demonstrating that even small e-commerce firms can achieve data-driven intelligence using open-source tools.</p> |
| title | End-to-End Data Engineering Pipeline for E-Commerce Analytics |
| url | https://doi.org/10.5281/zenodo.17466536 |