Transforming Bank’s Data using PySpark
“Combining similar types of information hierarchically not only improves data transfer efficiency but also simplifies decoding for downstream teams.”
This article, part of a series on designing a Data Lakehouse for a bank, focuses on transforming data using PySpark to prepare it for Kafka. By hierarchically combining columns into key-value pairs, the data is compressed for efficient transfer and easier decoding downstream.