The data engineering team maintains the following code:Assuming that this code produces logically correct results...

This code is using the pyspark.sql.functions library to group the silver_customer_sales table by customer_id and then aggregate the data using the minimum sale date, maximum sale total, and sum of distinct order ids. The resulting aggregated data is then written to the gold_customer_lifetime_sales_summary table, overwriting any existing data in that table. This is a batch job that does not use any incremental or streaming logic, and does not perform any merge or update operations. Therefore, the code will overwrite the gold table with the aggregated values from the silver table every time it is executed. References:

https://docs.databricks.com/spark/latest/dataframes-datasets/introduction-to-dataframes-python.html

https://docs.databricks.com/spark/latest/dataframes-datasets/transforming-data-with-dataframes.html

https://docs.databricks.com/spark/latest/dataframes-datasets/aggregating-data-with-dataframes.html