sabareh/dag-pipeline-with-dbt

End-to-End ELT Pipeline Implementation Using dbt, Snowflake, and Airflow

Project Overview:

The project focuses on the development and deployment of an ELT (Extract, Load, Transform) pipeline utilizing industry-standard tools such as dbt (data build tool), Snowflake, and Airflow. The pipeline is designed to handle the transformation and loading of data from source tables to final data marts, ensuring efficient data processing and robust data management.

Key Components and Features:

1. Data Modeling and Design:

   • Developed and implemented basic data modeling techniques, including the creation of fact tables and data marts.
   • Designed the schema to support efficient data retrieval and reporting.

2. Source and Staging Tables:

   • Created source tables to ingest raw data.
   • Developed staging tables that reference the source tables for initial data transformations and cleaning.

3. Transformation Logic:

   • Implemented mart tables that perform various transformations to convert raw data into business-relevant insights.
   • Wrote custom macros in dbt to handle complex transformation logic based on specific business requirements.

4. Snowflake RBAC:

   • Applied Snowflake Role-Based Access Control (RBAC) concepts to ensure secure and controlled access to data.

5. Orchestration with Airflow:

   • Utilized Apache Airflow to orchestrate the dbt project, ensuring seamless execution of data workflows.
   • Deployed the pipeline using Airflow's Astronomer Cosmos to manage and monitor the dbt jobs effectively.

Tools and Technologies:

• dbt (data build tool): For transformation logic and data modeling.
• Snowflake: For data warehousing and storage.
• Apache Airflow: For workflow orchestration and scheduling.
• Astronomer Cosmos: For deploying and managing Airflow workflows.

Project Achievements:

• Successfully created a robust and scalable ELT pipeline that integrates seamlessly with modern data stack components.
• Enhanced data processing efficiency through well-designed data models and transformation logic.
• Improved data security and access management using Snowflake RBAC.
• Streamlined workflow orchestration and monitoring using Airflow and Astronomer Cosmos.

Conclusion:

This project demonstrates the effective use of dbt, Snowflake, and Airflow to build an end-to-end ELT pipeline. The implementation highlights the importance of data modeling, secure data management, and efficient workflow orchestration in modern data engineering practices. The successful deployment and execution of this pipeline provide a strong foundation for future data integration and transformation projects.