Collection of data on Formula One Racing

Foundation Workspace for Airflow, Spark, Hive, and Azure Data Lake Gen2 via Docker

Azure-based solution for ingesting and analyzing Formula 1 data using Azure Data Lake Storage Gen2 and Databricks

The data engineering project aims to migrate a company's on-premises database to Azure, leveraging Azure Data Factory for data ingestion, transformation, and storage. The project will implement a three-stage storage strategy, consisting of bronze, silver, and gold data layers (Medalion architecture). Documentation of the project is in PDF file.

An end to End Implementation of the data lakehouse architecture

An end-to-end data engineering pipeline that fetches data from Wikipedia, cleans and transforms it with Apache Airflow and saves it on Azure Data Lake. Other processing takes place on Azure Data Factory, Azure Synapse and Tableau.

Integration of Covid-19 data utilising Azure Data Factory to perform data ingestion, transformation and storage activities. The goal of this guided project was to become familiar with Microsoft Azure technologies, including; Azure Data Factory(ADF), Azure Data Lake Storage Gen2, Azure SQL Database, Azure Blob Storage, Dataflow, Databricks, etc.