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azuredatabricks

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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.

  • Updated Sep 30, 2023
  • Jupyter Notebook

This project builds an End-to-End Azure Data Engineering Pipeline, performing ETL and Analytics Reporting on the AdventureWorks2022LT Database.

  • Updated Jan 24, 2024
  • Jupyter Notebook

Ingested Tokyo Olympic data into Azure Data Lake using Azure Data Factory. Enhanced data quality with Apache Spark on Azure Databricks. Optimized SQL queries on Synapse Analytics, reducing execution time. Developed engaging Power BI dashboards, boosting user engagement creating KPI's with DAX.

  • Updated Mar 5, 2024
  • Jupyter Notebook

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