Databricks Data Engineer Associate Question 295
Single answerA data engineering team is working with a Delta Lake table containing billions of rows of customer transaction data. They frequently query the table for a specific column, 'transaction_date', to filter data for analytics. The team wants to optimize query performance for these frequent date-based filters. Why would implementing Z-Ordering on the 'transaction_date' column be beneficial for this use case?
- A
Z-Ordering helps to distribute data evenly across all partitions, reducing skew.
- B
Z-Ordering clusters related data together in the same files, improving query performance for filters on the specified column.
- C
Z-Ordering automatically compresses data more efficiently for storage optimization.
- D
Z-Ordering ensures that Delta Lake tables are always fully cached in memory for faster access.
Show answer and explanation
Correct answer: B
Explanation
Z-Ordering is a file-level optimization in Delta Lake that clusters related data together within the same files based on specified columns. By colocating rows with similar values in the 'transaction_date' column, the query engine can skip irrelevant files and reduce the amount of data read during date-based filters. This improves query performance for the team's use case.
- A. Incorrect.
Z-Ordering does not focus on distributing data evenly across partitions; it is designed to colocate data in storage for column-specific optimization.
- B. Correct.
This is correct. Z-Ordering reorganizes data within files to colocate rows with similar values in the specified column(s), which reduces the amount of data read during queries.
- C. Incorrect.
While Z-Ordering can lead to better compression as a side effect, its primary goal is to optimize query performance by rearranging data, not storage compression.
- D. Incorrect.
Z-Ordering does not ensure that tables are cached in memory. Caching is a separate process and is not directly related to Z-Ordering.