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banking-data

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A complete mini-project demonstrating how to process, clean, and analyze 100,000 synthetic bank transaction records using PySpark in Databricks. It includes real-world data engineering tasks like data ingestion, null handling, feature engineering, transaction grouping, and business-level reporting, with output stored in Parquet format for BI-ready.

  • Updated Aug 2, 2025
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