iBalajiShanmugam/formual1
"Explore Formula 1 data analytics with this project. Leveraging the Ergast API, it utilizes Databricks Spark for ingestion, transformation, and analysis. ADLS acts as the storage layer, while Power BI visualizes the ADLS presentation layer. Uncover insights in the world of Formula 1 through powerful data analytics."
Formula 1 Data Analytics Project
This project aims to perform data analytics on Formula 1 racing data using the Ergast API as the data source. The architecture of the project is as follows:
Architecture Overview
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Ergast API: The Ergast API serves as the primary data source for Formula 1 racing data.
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ADLS RAW layer: The data from the Ergast API is ingested into the Azure Data Lake Storage (ADLS) RAW layer, which acts as the initial storage layer for the raw data.
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Ingest (Databricks Spark): Using Databricks Spark, the data is ingested from the ADLS RAW layer, performing necessary data transformations and cleaning operations.
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ADLS ingest layer: The transformed data is stored in the ADLS ingest layer, serving as an intermediate storage layer for further processing.
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Transform (Databricks Spark): Databricks Spark is used again to perform additional data transformations and aggregations on the data stored in the ADLS ingest layer.
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ADLS presentation layer: The transformed and processed data is stored in the ADLS presentation layer, providing a structured and optimized format for analysis.
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Analyse (Databricks): Leveraging the power of Databricks, the data in the ADLS presentation layer is analyzed using various analytical techniques and algorithms.
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ADLS presentation layer to Power BI: The analyzed data from the ADLS presentation layer is visualized and presented using Power BI for intuitive and interactive data exploration.