Production Fact and Dimension Joining Pipeline

How do we join fact orders with customer and date dimensions and project specific clean reporting columns?

Intermediate⏱ 3 Minutes⭐ 520 XP

🤔 THE QUESTION

How do we join fact orders with customer and date dimensions and project specific clean reporting columns?

💡 WHAT IS IT?

A production star-schema enrichment joining fact tables with customer and date dimensions with explicit projection.

🎯 WHAT IS IT USED FOR?

Data warehouse gold-layer dimensional modeling and executive BI reporting feeds.

💻 EXAMPLE

from pyspark.sql.functions import col

fact_enriched = fact_orders \
    .join(dim_customer, fact_orders.cust_key == dim_customer.cust_key, "inner") \
    .join(dim_date, fact_orders.date_key == dim_date.date_key, "left") \
    .select(
        fact_orders.order_id,
        dim_customer.customer_name,
        dim_date.calendar_year,
        fact_orders.gross_amount
    )

🎯 Mission Objectives

Practice typing production-grade PySpark code for Production Fact and Dimension Joining Pipeline.

✓ Join fact table with customer and date dimensions
✓ Select curated reporting columns
✓ Build gold-layer star schema view

🚀 Start Mission

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