> OVERVIEW()

This project focused on architecting and designing a Data Warehouse (DWH) capable of supporting reporting and Online Analytical Processing (OLAP) across business and environmental datasets. The goal was to build a scalable, analytics-ready data platform that integrates Yelp business data with climate records to analyze how weather conditions influence customer behavior, satisfaction, and business performance.

> BUSINESS_PROBLEM()

Small businesses often struggle to understand how external factors such as weather, seasonality, and location conditions impact customer engagement and sales performance.

By combining business, customer, and climate data, this warehouse enables analysts to answer questions like:
• How do temperature or precipitation levels affect customer check-ins and review frequency?
• Do certain businesses perform better during specific climate patterns or seasons?
• Can we detect customer satisfaction trends based on environmental conditions?

> DATA_SOURCES()

• Yelp Data: Business information, customer reviews, ratings, tips, and check-ins for restaurants and local businesses across multiple US cities.
• Climate Data: Historical temperature and precipitation observations, used to analyze environmental impact on business engagement and performance.

Challenge: Yelp data is semi-structured JSON and relational CSV, while climate data is time-series based. The pipeline required schema normalization and temporal alignment.

> TOOLS_AND_TECH_STACK()

• Snowflake – cloud data warehouse for staging, ODS, and dimensional modeling
• SQL – data transformation, schema design, and star schema modeling
• Python (Pandas) – preprocessing, feature engineering, climate API ingestion
• Airflow (conceptual) – ETL pipeline orchestration strategy
• Tableau / Power BI – visualization and performance dashboarding

\