How do we build a complete financial analytics pipeline computing previous spend, cumulative spend, and growth?
An analytics pipeline combining lag(), cumulative running sum(), and difference arithmetic.
Enterprise customer spend analytics, credit underwriting models, and revenue expansion forecasting.
from pyspark.sql.window import Window
from pyspark.sql.functions import col, lag, round, sum
user_window = Window.partitionBy("user_id").orderBy("transaction_date")
running_window = Window.partitionBy("user_id").orderBy("transaction_date").rowsBetween(Window.unboundedPreceding, Window.currentRow)
analytics_df = transactions \
.withColumn("prev_spend", lag(col("amount"), 1).over(user_window)) \
.withColumn("cum_spend", sum(col("amount")).over(running_window)) \
.withColumn("spend_growth", round(col("amount") - col("prev_spend"), 2))Practice typing production-grade PySpark code for Production Financial & Retention Window Pipeline.