Databricks Data Engineer Professional Question 185
Select 3A data engineering team is designing a normalized data model for a retail application in Databricks. They create a 'Products' table with product details and a separate 'ProductCategories' lookup table to store category details. The team wants to optimize join performance between the 'Products' and 'ProductCategories' tables for reporting use cases. Which of the following trade-offs should the team consider when using this normalized model?
- A
Increased join complexity can lead to slower query performance for large datasets.
- B
Simplified schema design improves data integrity with a single source of truth for categories.
- C
Denormalization would reduce the need for joins, but could lead to data redundancy.
- D
Lookup table normalization increases storage costs due to data duplication.
- E
Joining normalized tables is always faster than querying a denormalized table.
Show answer and explanation
Correct answers: A, B, C
Explanation
When implementing lookup tables in a normalized data model, the trade-offs include increased join complexity and potentially slower query performance due to the need to join multiple tables. However, normalization ensures data integrity and reduces data redundancy. Denormalization could improve query performance but at the cost of introducing redundancy and complicating updates. Understanding these trade-offs is crucial for optimizing data models in Databricks for specific workloads.
- A. Correct.
Correct: Joining normalized tables can increase query complexity and slow down performance, especially with large datasets, as each join requires additional computational overhead.
- B. Correct.
Correct: Normalization provides a single source of truth for categories, ensuring data integrity by avoiding duplication of category details across multiple rows.
- C. Correct.
Correct: Denormalization reduces the need for joins but creates redundancy, which can make updates more complex and increase storage usage.
- D. Incorrect.
Incorrect: Lookup table normalization generally reduces storage costs because data is not duplicated across rows. Instead, a foreign key reference is used.
- E. Incorrect.
Incorrect: Joining normalized tables is not always faster. Denormalized tables can often provide better performance for read-heavy workloads by avoiding joins.