Process Common Crawl data with Python and Spark
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Updated
Feb 11, 2025 - Python
Process Common Crawl data with Python and Spark
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The project harnessed an ETL multi-hop architecture, ingesting data from the Ergast API into a storage backed by Azure Data Lake. The process involved weekly ingestion of bronze layer data as cutover and delta files. Raw data, in varied formats, was transformed using Azure Databricks PySpark notebooks into enriched Silver and Gold layers.
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