To solve the complex problem of aggregating subscriber data by city based on both salary and age median, traditional SQL functions prove inadequate due to their limitations.
This challenge necessitates employing User Defined Aggregate Functions (UDAFs) in Spark, which enable custom calculations and facilitate the median calculations, overcoming Spark SQL's inherent restrictions.
By leveraging the UDAF framework, we can efficiently determine the list of subscribers exceeding the median salary while remaining younger than the median age in each city.
Through this experiment, I compared various data engineering techniques based on performance and ease of implementation, highlighting the importance of adaptability in data aggregation scenarios.
#data-engineering #aggregation-techniques #spark-sql #user-defined-aggregate-functions #median-calculation
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