Scalable Cloud Data Analytics & Processing Pipeline

A scalable cloud-based data processing workflow designed to ingest, clean, and query structured datasets efficiently for downstream machine learning tasks.

Key Contributions & Architecture:

Storage & Data Lake Setup: Structured raw and processed data partitions using cloud storage standards.

SQL Analytics & Extraction: Executed optimized analytical SQL queries to aggregate key performance indicators and prepare feature tables.

Automation & Best Practices: Followed cloud architecture principles to maintain reliability, minimal operational cost, and high throughput during data transformations.

Technologies Used: Python, SQL, Cloud Storage (AWS S3/Athena concepts), Data Cleaning, Linux.