Capstone Part 9: Partitioned Lakehouse Write with Dynamic Overwrite

How can we write the gold data mart to cloud storage partitioned by event_date with dynamic partition overwrite?

Production⏱ 3 Minutes⭐ 880 XP

🤔 THE QUESTION

How can we write the gold data mart to cloud storage partitioned by event_date with dynamic partition overwrite?

💡 WHAT IS IT?

Configuring dynamic partition overwrite guarantees that only modified date partitions are refreshed.

🎯 WHAT IS IT USED FOR?

Publishing production tables safely in idempotent daily batch pipeline runs.

💻 EXAMPLE

spark.conf.set("spark.sql.sources.partitionOverwriteMode", "dynamic")
df_final_output.write.partitionBy("event_date").mode("overwrite").parquet("s3://lakehouse/gold/mart_regional_daily/")

🎯 Mission Objectives

Practice typing production-grade PySpark code for Capstone Part 9: Partitioned Lakehouse Write with Dynamic Overwrite.

✓ Dynamic partition overwrite
✓ partitionBy('event_date')
✓ Idempotent warehouse publishing

🚀 Start Mission

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