End-to-End Azure Project using Microsoft Fabric.
I designed and implemented a comprehensive ELT pipeline utilizing Snowflake, Amazon S3, Apache Airflow, and DBT to streamline data ingestion, processing, and analytics for a data-driven organization.
Technologies Used:
Data Warehouse: Snowflake
Data Storage: Amazon S3
Workflow Orchestration: Apache Airflow
Data Transformation: DBT (Data Build Tool)
Visualization: Tableau, Looker
Programming/Markup: Python (Airflow), SQL (DBT and Snowflake)
Architecture and Workflow:
Data Ingestion (Extract):
Extracted data from multiple sources, including REST APIs, third-party systems, and flat files.
Stored data in Amazon S3 buckets, organized by date, source, and format.
Data Loading (Load):
Utilized Airflow DAGs to automate loading raw data from S3 into Snowflake staging tables.
Data Transformation (Transform):
Employed DBT to define and execute SQL-based transformations in Snowflake.
Created modular and reusable DBT models for:
Cleaning and standardizing data.
Joining data from multiple sources.
Orchestration and Scheduling:
Designed Airflow workflows to manage the pipeline end-to-end, including:
Scheduling data extraction jobs.
Loading data into Snowflake.
Triggering DBT transformations.
Analytics and Insights:
Enabled near-real-time reporting and analytics by transforming raw data into actionable insights.
Data was consumed by BI tools like Tableau and Looker for visualization.
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Posted Apr 17, 2025
Architected and implemented a robust ELT pipeline leveraging Snowflake, Amazon S3, Airflow, and DBT to enable scalable and automated data processing
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