🏠 dbt Project: Airbnb Data Modeling with Snowflake

Welcome to this dbt project, which demonstrates how to model Airbnb data in a Data Warehouse environment using Snowflake.

This setup is inspired by:

• The jaffle_shop example project, which outlines a simple pattern for staging, transforming, and analyzing data.
• The Udemy course Complete dbt (Data Build Tool) Bootcamp: Zero to Hero, created by the dbt Learn Team.

The project follows best practices for:

1. Data Loading: Ingesting CSV data into Snowflake.
2. Staging: Creating staging models to clean and prepare raw data.
3. Transforming: Building intermediate and mart layers to enrich and aggregate data.
4. Analyzing: Leveraging dbt to easily manage models, run tests, and generate documentation.

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📖 Overview

The goal of this project is to:

1. Ingest Airbnb data from a CSV file into Snowflake.
2. Transform raw data into analytics-friendly models using dbt.
3. Provide dimensional models (dimensions, facts) that can be easily queried and analyzed.

Key components include:

• Raw Data: Sourced from CSV files (e.g., input_data.csv).
• Snowflake: Serves as the data warehouse to store both the raw data and transformed models.
• dbt (Data Build Tool): Handles data transformations, testing, and documentation within Snowflake.

This README outlines how everything is structured, following a simplified approach similar to jaffle_shop.

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🏗 Project Structure

The project is organized into several types of dbt models:

• Source Models (src_*.sql)
  These models define your data sources (CSV files loaded into Snowflake). They provide a clear reference for all your raw data tables.

• Staging Models (stg_*.sql)
  Inspired by jaffle_shop, these models clean and unify data from the source layer. They typically handle tasks like:

  • Renaming or casting fields.
  • Filtering out invalid records.
  • Standardizing date formats and keys.

• Dimension Models (dim_*.sql)
  These are transformations of your staged data into dimensions, holding descriptive attributes. For example, dim_hosts_cleansed.sql represents cleansed and standardized information about Airbnb hosts.

• Fact Models (fct_*.sql)
  These models aggregate data into tables optimized for analytics. Fact models often contain metrics or measures (e.g., a reviews fact table that stores review counts, ratings, etc.).

• Intermediate/Join Models (dim_listings_w_hosts.sql)
  When you need to combine data from multiple dimensions (e.g., listings with their corresponding hosts), these models serve as an intermediate step, making further analysis simpler.

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📂 File Summary

Below is a quick guide to the primary SQL models:

• src_hosts.sql — Defines the source of host data (raw form).
• src_listings.sql — Defines the source of listing data (raw form).
• src_reviews.sql — Defines the source of review data (raw form).
• dim_hosts_cleansed.sql — Cleans and transforms host data.
• dim_listings_cleansed.sql — Cleans and transforms listing data.
• dim_listings_w_hosts.sql — Joins listings and hosts for broader context.
• fct_reviews.sql — Provides an aggregated fact table of reviews.

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⚙️ Data Pipeline

1. Ingest CSV Data into Snowflake

   • Upload or load your input_data.csv into a Snowflake table (e.g., RAW_AIRBNB_DATA).
   • Create external stages or use Snowflake’s load methods (e.g., COPY INTO) to bring the data into your environment.

2. Build dbt Models

   • Source models (src_*.sql) reference the loaded raw data in Snowflake.
   • Staging models (stg_*.sql) transform and clean this data according to best practices from jaffle_shop.
   • Dimension and fact models (dim_*.sql, fct_*.sql) organize the data into analysis-ready tables.

3. Analyze and Visualize

   • Once your models are materialized in Snowflake, use BI or data visualization tools (e.g., Looker, Tableau, or Mode) to analyze your Airbnb metrics and KPIs.

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🚀 Getting Started

Prerequisites

1. Snowflake account with the necessary warehouse and database.
2. dbt installed (CLI or Cloud version).
3. A configured profiles.yml to connect dbt to your Snowflake environment.

Quickstart

1. Clone this repository and navigate into the project directory:

   git clone https://github.com/your-account/airbnb-dbt-snowflake.git
   cd airbnb-dbt-snowflake

2. Install dbt dependencies:

   pip install dbt-snowflake

3. Run dbt commands:

   dbt deps
   dbt seed
   dbt run
   dbt test
   dbt docs generate

🔨 Customization & Inspiration

• Adapt to Your Data: While this project focuses on Airbnb datasets, you can customize the staging, dimension, and fact models to fit any CSV-based data.
• Inspired by jaffle_shop: Check out jaffle_shop to see how the dbt-labs team organizes source, staging, and modeling layers. It’s a great starting point for any new analytics project.
• Modular & Reusable: Because each stage is decoupled, you can easily swap out or update specific models without affecting the rest of the pipeline.

📄 Additional Resources

• dbt Documentation
• Snowflake SQL Reference
• jaffle_shop on GitHub

⚖️ License

This project is licensed under the MIT License. Feel free to use and adapt any part of this project in your own data workflows.