This project demonstrates an end-to-end ETL pipeline built using Snowflake modern layered architecture. pipeline ingests retail data from CSV files, validates , transforms the data, loads clean records into curated tables, stores invalid records in reject tables, supports incremental loading using MERGE, and maintains execution logs for monitoring.
A metadata-driven, CDC-based Medallion architecture pipeline that ingests a Spotify-style streaming warehouse from Azure SQL, processes it through Bronze → Silver → Gold using Databricks Autoloader and Delta Live Tables, and models it as a Star Schema in Unity Catalog — with SCD Type 2 history, Git-based CI/CD, and automated failure alerting.
End-to-end Azure data engineering pipeline using Spotify dummy data with Medallion Architecture, Databricks, Delta Live Tables, and incremental loading.
Configuration-driven ETL framework in Python — YAML-defined pipelines, plugin architecture, incremental loading, data quality checks, DuckDB warehouse.
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