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Overview
Overview
This project simulates a real-world automotive company generating transactional data in real time.
The goal was to design and implement a complete data pipeline and data warehouse solution to support business decision-making
Architecture
Architecture
The pipeline ingests data from PostgreSQL, orchestrates workflows using Airflow, transforms data with dbt, and stores it in Snowflake for analytics and reporting.
Data Pipeline Flow
Data Pipeline Flow
Extract data from PostgreSQL (transactional system)
Orchestrate workflows using Airflow DAGs
Load raw data into Snowflake
Transform and model data using dbt
Validate and document data models
Build dashboards in Looker Studio
Results
Results
Built a scalable and automated data pipeline
Enabled structured data modeling for analytics
Improved data accessibility for business insights
Simulated a real production-ready data environment
Technologies Used
Technologies Used
PostgreSQL
Apache Airflow
Snowflake
dbt
Docker
Looker Studio
Technical Skills
Technical Skills
Python (pandas)
SQL (PostgreSQL, BigQuery)
Airflow
dbt
Snowflake
Databricks
Docker
Git & GitHub
AWS S3 / GCS
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