Airbnb analytics project
Project Overview
This project implements an end-to-end analytics pipeline using dbt and Snowflake, transforming raw Airbnb data into a structured and reliable analytical layer. The pipeline is designed to reflect real-world analytics engineering practices, with a strong focus on data modeling, data quality, and maintainability. The objective is not only to clean and transform data, but to create a scalable foundation that supports consistent and trustworthy business analysis. (Datasets downloaded from Inside Airbnb)
Key points:
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Data modeling: Designed a layered architecture (raw → staging → marts) to clearly separate data cleaning from business logic, improving maintainability and enabling reusable transformations.
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Transformation logic: Standardized semi-structured CSV data using SQL-based transformations, ensuring consistency across datasets and reducing downstream complexity.
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Data quality & testing: Implemented a combination of built-in, custom, and unit tests to enforce data reliability and catch inconsistencies early in the pipeline.
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Incremental processing & history tracking: Used incremental models to optimize compute performance and reduce processing time, and implemented snapshots to track changes in listing attributes over time.
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Reusable components: Developed macros and parameterized logic to reduce duplication and improve scalability of transformations.
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Modern data stack: Built a modular pipeline leveraging dbt and Snowflake, following patterns commonly used in production analytics environments.
Data model
The analytical layer is structured using a star schema to support efficient querying and business reporting:
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Dimensions: hosts, listings, neighbourhoods
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Fact table: reviews (one row per review event)
This design enables flexible analysis of booking activity across locations, host performance, and listing characteristics, while keeping query complexity low.
Design approach
The project focuses on demonstrating how core dbt features can be applied to build a maintainable and production-oriented data model. To keep the scope focused, some patterns (such as relationship tests and advanced data quality checks) are applied to a subset of models. In a production environment, these practices would be consistently enforced across all layers of the pipeline.
The emphasis is on clarity of design decisions and implementation patterns, rather than exhaustive coverage.