Databricks Data Engineer Professional Question 57
Single answerYou are designing a data pipeline for a retail company that processes sales data across multiple stores and regions. The sales data is queried frequently to generate reports by region and store, and new data is ingested daily. Which of the following strategies would be the most appropriate for partitioning the data to optimize query performance and data management?
- A
Partition the data by the sales year.
- B
Partition the data by region and store.
- C
Partition the data by store and date.
- D
Do not partition the data and keep it in a single file.
Show answer and explanation
Correct answer: B
Explanation
Partitioning strategies should be aligned with the most frequent query patterns and the way data is ingested or managed. In this case, partitioning by region and store is the most appropriate choice because it matches the query access patterns and helps in efficient data retrieval for generating reports. Additionally, it avoids creating too many small partitions or overly broad partitions that would negatively impact performance.
- A. Incorrect.
Partitioning by sales year would not optimize queries for accessing data by region and store because the queries are more granular than just year-level data.
- B. Correct.
Partitioning by region and store aligns with the query patterns and optimizes the retrieval of data for reports segmented by these dimensions. It also ensures that data is well-organized for both read and write operations.
- C. Incorrect.
Partitioning by store and date might help with daily ingestion, but it would not optimize the frequent queries for reports by region and store. Additionally, the 'date' column may result in too many small partitions, affecting performance.
- D. Incorrect.
Not partitioning the data and keeping it in a single file would result in poor query performance due to the lack of data pruning and difficulty in managing large datasets.