How can we execute the full end-to-end enterprise data engineering workflow from raw ingestion to optimized gold export in PySpark?
The master data engineering capstone pipeline integrating schema parsing, null handling, deduplication, broadcast joins, window features, and partitioned writes.
Production enterprise Lakehouse architectures processing mission-critical data pipelines at scale.
spark.conf.set("spark.sql.sources.partitionOverwriteMode", "dynamic")
w = Window.partitionBy("event_id").orderBy(col("event_timestamp").desc())
df_pipeline = df_raw.withColumn("data", from_json(col("payload"), event_schema)).select("data.*").withColumn("event_timestamp", to_timestamp(col("timestamp"))).withColumn("rn", row_number().over(w)).filter(col("rn") == 1).drop("rn").join(broadcast(df_customers), "user_id", "left").coalesce(25)
df_pipeline.write.partitionBy("event_date").mode("overwrite").parquet("s3://lakehouse/gold/production_master/")Practice typing production-grade PySpark code for Capstone Part 10: Complete Enterprise Production Data Pipeline.