🏗️ dbt Data Warehouse Projects

A collection of dbt (Data Build Tool) projects focused on data transformation, modelling, and building scalable data warehouses. This repository is actively growing — new projects are added regularly!

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👨‍💻 About This Repository

This repository is dedicated to dbt-based data engineering projects. Each project demonstrates real-world data transformation workflows, dimensional modelling, and best practices in analytics engineering using dbt Core/Cloud.

Whether you're here to learn, collaborate, or explore — feel free to browse through the projects below!

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📁 Projects

#	Project Name	Description	Warehouse	Status
1	dbt-datawarehouse-project	End-to-end dbt data warehouse project with staging, intermediate, and mart layers	—	✅ Complete
2	(Coming Soon)	—	—	🔄 In Progress
3	(Coming Soon)	—	—	📅 Planned

📌 Note: More projects will be added to this repo regularly. Star ⭐ the repo to stay updated!

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🛠️ Tech Stack

• dbt Core — Data transformation and modelling
• SQL — Core transformation language
• YAML — Schema definitions, sources, and tests
• Git — Version control for all models and configurations

Supported Data Warehouses

• PostgreSQL
• Snowflake
• BigQuery
• Redshift
• DuckDB

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🗂️ Standard Project Structure

Each dbt project in this repository follows this standard folder layout:

project-name/
├── dbt_project.yml          # Project configuration
├── packages.yml             # dbt package dependencies
├── profiles.yml             # Connection profile (sample only)
├── models/
│   ├── staging/             # Raw source cleaning (stg_*)
│   ├── intermediate/        # Business logic models (int_*)
│   └── marts/               # Final dimension & fact tables
│       ├── core/
│       ├── finance/
│       └── marketing/
├── seeds/                   # Static CSV reference data
├── snapshots/               # SCD Type 2 history tracking
├── tests/                   # Custom data quality tests
├── macros/                  # Reusable Jinja macros
└── README.md                # Project-specific documentation

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🚀 Getting Started

Prerequisites

• Python 3.8+
• dbt Core installed (pip install dbt-core)
• Access to a supported data warehouse

Clone the Repository

git clone https://github.com/Ritik574-coder/dbt-datawarehouse-project.git
cd dbt-datawarehouse-project

Install dbt

pip install dbt-core dbt-postgres   # or dbt-snowflake, dbt-bigquery, etc.

Configure Your Profile

Copy the sample profiles.yml and update with your warehouse credentials:

cp profiles.yml ~/.dbt/profiles.yml
# Edit ~/.dbt/profiles.yml with your connection details

Run a Project

cd <project-folder>
dbt deps          # Install packages
dbt seed          # Load seed/CSV files
dbt run           # Run all models
dbt test          # Run data quality tests
dbt docs generate && dbt docs serve   # View project documentation

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🧪 Testing & Documentation

All projects include:

• ✅ Generic Tests — not_null, unique, accepted_values, relationships
• ✅ Custom Tests — Business-logic specific SQL tests
• ✅ Source Freshness Checks — dbt source freshness
• ✅ dbt Docs — Auto-generated model documentation

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📌 Key dbt Concepts Covered

• Layered Architecture — Staging → Intermediate → Marts
• Slowly Changing Dimensions (SCD) — Using dbt Snapshots
• Incremental Models — Efficient large dataset processing
• dbt Seeds — Loading static reference data
• Jinja Macros — Reusable SQL logic
• dbt Packages — Using dbt-utils, dbt-expectations, etc.
• Source & Ref Functions — Dependency management
• Data Lineage — Auto-generated DAG visualization

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📬 Connect With Me

• GitHub: @Ritik574-coder

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📄 License

This repository is open-source and available under the MIT License.

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⭐ If you find this helpful, please consider starring the repository!