This project focuses on constructing a comprehensive data warehouse for Airbnb listings in Barcelona, providing a robust framework for data storage, organization, and analysis. The objective was to centralize data related to Airbnb properties, including listings, bookings, reviews, and host information, to facilitate in-depth analytical insights and decision-making processes.

The project involved several key stages:

1. Data Scraping: Utilizing Python scripts to scrape Airbnb data.
2. Data Loading: Employing pandas and SQLAlchemy to load the scraped data into a PostgreSQL database, setting the foundation for further processing and analysis.
3. Data Profiling: Conducting an initial assessment of the data using the Airbnb Data Dictionary as a reference to identify data quality issues and understand the dataset's structure and content.
4. Data Warehouse Design: Developing a data warehouse schema tailored to the needs of Airbnb data analysis, featuring two fact tables for reviews and bookings, alongside dimension tables for dates, listings, reviewers, locations, and hosts.
5. Data Transformation: Applying SQL statements to transform the raw data, ensuring it is properly formatted and aligned with the data warehouse schema.
6. Data Loading into Data Warehouse: Executing SQL scripts with insert statements to populate the data warehouse, making the data ready for analysis.

• Airbnb Dataset: The primary dataset for this project was obtained from Inside Airbnb (Get the Data), which provides detailed data on Airbnb listings and activities.
• Airbnb Data Dictionary: The Airbnb Data Dictionary was instrumental in understanding the dataset's attributes and guiding the data profiling stage.

• Python: For data scraping and initial data processing.
• Pandas & SQLAlchemy: For data manipulation and loading the data into PostgreSQL.
• PostgreSQL: As the relational database management system to store the initial datasets.
• SQL: For data transformation, schema creation, and data loading processes in the data warehouse.

The data warehouse is designed with analytical queries in mind, structured around two fact tables:

• Reviews Fact Table: Captures details about reviews made by guests.
• Bookings Fact Table: Contains information on bookings.

The supporting dimension tables are:

• Date Dimension: Holds information on dates to enable time-based analyses.
• Listings Dimension: Contains detailed information about the listings.
• Reviewers Dimension: Stores information about the reviewers.
• Location Dimension: Captures geographical information about the listings.
• Hosts Dimension: Contains details about the hosts of the listings.