Hybrid DataFrame & SQL Analytics Pipeline

How do we combine Parquet ingestion, DataFrame column transformations, and SQL aggregation in an analytics pipeline?

Advanced⏱ 3 Minutes⭐ 750 XP

🤔 THE QUESTION

How do we combine Parquet ingestion, DataFrame column transformations, and SQL aggregation in an analytics pipeline?

💡 WHAT IS IT?

A hybrid pipeline reading Parquet, transforming columns with DataFrame APIs, and executing SQL aggregation.

🎯 WHAT IS IT USED FOR?

Enterprise Lakehouse data marts combining DataFrame preprocessing with SQL reporting queries.

💻 EXAMPLE

from pyspark.sql.functions import col, upper

raw_df = spark.read.parquet("data/silver_sales")
raw_df.withColumn("region_code", upper(col("region"))) \
      .createOrReplaceTempView("v_clean_sales")

mart_df = spark.sql("""
    SELECT region_code, COUNT(*) AS txn_count, SUM(revenue) AS total_rev
    FROM v_clean_sales
    GROUP BY region_code
""")
mart_df.show()

🎯 Mission Objectives

Practice typing production-grade PySpark code for Hybrid DataFrame & SQL Analytics Pipeline.

✓ Ingest Parquet dataset
✓ Apply DataFrame string transformation
✓ Execute final aggregation via Spark SQL

🚀 Start Mission

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