https://github.com/nel-zi/francine_store
Built a scalable data pipeline for Francine Stores, enabling them to extract, clean, and load data from Aliexpress for real-time market trend analysis and smarter business decisions.
https://github.com/nel-zi/francine_store
datacleaning dataengineering dataextraction datamodeling etl etl-pipeline pandas
Last synced: 2 days ago
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Built a scalable data pipeline for Francine Stores, enabling them to extract, clean, and load data from Aliexpress for real-time market trend analysis and smarter business decisions.
- Host: GitHub
- URL: https://github.com/nel-zi/francine_store
- Owner: Nel-zi
- Created: 2025-01-12T20:12:30.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-13T16:44:14.000Z (4 months ago)
- Last Synced: 2025-02-17T20:34:32.768Z (3 months ago)
- Topics: datacleaning, dataengineering, dataextraction, datamodeling, etl, etl-pipeline, pandas
- Language: Jupyter Notebook
- Homepage:
- Size: 8.74 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Francine_store
Project overview
● Francine Stores, a laptop vendor on Aliexpress, aims to analyze product brand
trends on the platform to better understand market demands. They require
a data engineer to scrape, transform, and load Aliexpress data into their
database for advanced analysis.
Business Problem
● The main challenge is extracting accurate and current data from Aliexpress,
which is dynamic and complex due to its large scale and frequent updates.
Objectives
● To develop a scalable and robust system that periodically extracts data from
Aliexpress, ensuring the data is clean, well-structured, and updated.
Benefits
1. Market Insight: Understand real-time market trends and consumer
preferences.
2. Strategic Decisions: Data-driven strategies for stock management and
marketing.
3. Competitive Advantage: Staying ahead by leveraging up-to-date data.