Section 1 - Instruction

You've now covered the core components of high-performance analytical systems: dimensional models, columnar formats, and partitioning. Let's practice combining these techniques to design a truly fast data warehouse.

Engagement Message

Ready to put it all together?

Section 2 - Practice

Type

Sort Into Boxes

Practice Question

Sort these data elements into the correct table type for a sales data warehouse.

Labels

  • First Box Label: Fact Table
  • Second Box Label: Dimension Table

First Box Items

  • Sale Amount
  • Units Sold
  • Order Date

Second Box Items

  • Customer Name
  • Product Category
  • Store Location
Section 3 - Practice

Type

Multiple Choice

Practice Question

Your team needs to analyze sales trends over the last quarter. The data is stored in Parquet files partitioned by month. Why is this design so efficient?

A. It reads all data row by row for completeness. B. It only scans the relevant monthly partitions and necessary columns. C. It stores data in a highly normalized format to save space. D. It avoids using indexes, which slow down writes.

Suggested Answers

  • A
  • B - Correct
  • C
  • D
Section 4 - Practice

Type

Fill In The Blanks

Markdown With Blanks

Let's complete the sentence about designing an analytical system.

A [[blank:star]] schema organizes data into facts and dimensions. Storing this data in a [[blank:columnar]] format like Parquet and [[blank:partitioning]] it by date makes queries extremely fast.

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