Build a movie recommendation data pipeline using Azure services for efficient data ingestion, transformation, and orchestration. Utilize Azure Blob Storage, Azure Databricks, and Azure Data Factory to implement collaborative filtering and PySpark ML for accurate movie recommendations.

⚡️ Jupyter Notebook CI powered by ☁️ Azure ML (MSFT Internship 2019)

Databricks ETL Pipeline for retrieving and processing NI TestStand test results, featuring a well-documented notebook for ETL operations, Data Lake for storage, Spark SQL+Python for transformations, and Power BI as the final visualization of factory metrics.

building a real-world data pipeline in Azure Data Factory (ADF) dataset provided by https://www.ecdc.europa.eu/ ingesting data from sources such as HTTP and Azure Blob Storage into Azure Data Lake Gen2 using ADF. transformed data and loaded transformed data using Databricks Notebook Activity in Azure Data Factory (ADF) and load into Azure Data L…