How can we overwrite only the partitions modified in the current batch without deleting the entire historical dataset?
Setting partitionOverwriteMode to 'dynamic' ensures Spark only replaces directories matching the incoming batch partition keys.
Idempotent daily backfills and updates in partitioned lakehouse tables without risk of wiping full table history.
spark.conf.set("spark.sql.sources.partitionOverwriteMode", "dynamic")
df_daily_batch.write.partitionBy("sale_date").mode("overwrite").parquet("s3://lakehouse/gold/daily_sales/")Practice typing production-grade PySpark code for Dynamic Partition Overwrite Mode.