Tableau Integration Exercise: Joins vs Blends, Filters, and Order of Operations

Data

You have two sources.

Primary (Orders)

• Columns: OrderID, State, Category, Sales
• Rows:
  1. (O1, CA, Furniture, 100)
  2. (O2, CA, Office Supplies, 200)
  3. (O3, NY, Technology, 150)
  4. (O4, TX, Furniture, 300)
  5. (O5, CA, Technology, 50)

Secondary (Targets)

• Columns: State, Category, MonthlyTarget
• Rows: A) (CA, Furniture, 500) B) (CA, Technology, 400) C) (NY, Furniture, 250) D) (FL, Technology, 600)

Tasks

1. Physically join Orders ↔ Targets on State AND Category.
   • Inner join: Which rows remain? What are SUM(Sales) and SUM(MonthlyTarget) by State, Category?
   • Left join (Orders → Targets): Which rows remain? What are SUM(Sales) and SUM(MonthlyTarget) by State, Category?
2. Data blending: Use Orders as Primary and Targets as Secondary. Link on State and Category.
   • With State and Category on Rows, and SUM(Sales) and SUM(MonthlyTarget) on Columns, explain exactly how Tableau computes the view.
   • Specify how unmatched members appear (e.g., NY–Technology; TX–Furniture; FL–Technology) and whether targets/sales show as null or zero. Explain why.
3. Show how the results change under each filter and explain why using Tableau’s order of operations.
   • (a) Dimension filter Category = 'Furniture' from Orders (primary)
   • (b) Dimension filter Category = 'Furniture' from Targets (secondary)
   • (c) Context filter State = 'CA' on Orders
   • (d) Measure filter SUM(Sales) > 150
   • (e) Data source filter on Targets keeping only Category IN ('Furniture','Technology')
4. Describe two pitfalls where blending and joining yield different aggregates due to level of detail and many-to-many relationships (e.g., duplicate State–Category rows in Targets). Provide a concrete example based on the given data.
5. When would you prefer blending over joining in Tableau? Address cross-database analysis, required granularity, linking-field behavior, performance/caching, and calculation limitations (e.g., FIXED LODs, table calcs) in secondary sources.