Project: Data Aggregator

In this project, you'll build a data aggregator that scrapes multiple sources and combines the data into a unified dataset.

Project Overview

Step 1: Unified Data Model

Step 2: Source Scrapers

Step 3: Aggregator Class

Step 4: Parallel Scraping

Step 5: Data Normalization

Step 6: Storage & Export

Step 7: Complete Pipeline

Enhancement Ideas

Key Takeaways

• Design a unified data model that works across sources
• Use a base class for consistent scraper interfaces
• Generate unique IDs for deduplication
• Parallelize scraping for speed
• Normalize data from different sources
• Export to multiple formats (JSON, CSV)
• Handle failures gracefully per source
• This pattern applies to many aggregation use cases