Production Spark SQL Analytical Pipeline

How can we construct an enterprise analytical pipeline combining CTEs, window functions, and catalog persistence in Spark SQL?

Advanced⏱ 3 Minutes⭐ 890 XP

🤔 THE QUESTION

How can we construct an enterprise analytical pipeline combining CTEs, window functions, and catalog persistence in Spark SQL?

💡 WHAT IS IT?

Executing an end-to-end CTE transformation and saving the result via saveAsTable() creates managed warehouse assets.

🎯 WHAT IS IT USED FOR?

Daily executive summary mart generation in automated lakehouse batch workflows.

💻 EXAMPLE

query = """WITH daily_summary AS (SELECT sale_date, region, COUNT(order_id) AS total_orders, SUM(amount) AS total_revenue FROM default.analytics_sales GROUP BY sale_date, region) SELECT sale_date, region, total_orders, total_revenue, AVG(total_revenue) OVER (PARTITION BY region ORDER BY sale_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS rolling_7d_avg FROM daily_summary"""
df_mart = spark.sql(query)
df_mart.write.mode("overwrite").saveAsTable("default.mart_regional_revenue")

🎯 Mission Objectives

Practice typing production-grade PySpark code for Production Spark SQL Analytical Pipeline.

✓ End-to-end SQL mart pipeline
✓ saveAsTable() persistence
✓ Production data mart creation

🚀 Start Mission

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