Databricks Data Engineer Professional Question 56
Select 2You are designing a large-scale data pipeline in Databricks to process sales transactions for a global e-commerce company. The dataset contains billions of rows, and frequent queries filter the data by region and transaction_date. To optimize query performance and avoid data skew, which of the following strategies should you use for partitioning the data?
- A
Partition the data by
regionand thentransaction_date. - B
Partition the data by
transaction_dateonly. - C
Partition the data by
regiononly. - D
Partition the data by a hash of
regionandtransaction_date. - E
Do not partition the data, as Databricks automatically optimizes all queries.
Show answer and explanation
Correct answers: A, D
Explanation
To optimize query performance, partitioning should be based on the filtering patterns and data distribution. Partitioning by region and transaction_date targets the common filters, while using a hash-based approach can prevent data skew and ensure balanced partition sizes. Ignoring partitioning or partitioning on only one column overlooks query patterns and can lead to inefficiencies.
- A. Correct.
Partitioning by
regionand thentransaction_dateis a suitable strategy because both fields are commonly used in filters. This approach ensures better query performance by reducing the data scanned during queries. However, it may lead to slightly more complex metadata management for highly granular data. - B. Incorrect.
Partitioning only by
transaction_datemight help with queries filtering by date, but it ignores theregionfilter, which could lead to scanning unnecessary data for region-specific queries. - C. Incorrect.
Partitioning only by
regionmight improve region-specific queries, but it does not optimize for queries that filter by date, leading to inefficient scans. - D. Correct.
Partitioning by a hash of
regionandtransaction_datecan effectively balance the data across partitions and avoid data skew, particularly when there is a high cardinality intransaction_dateor an uneven distribution of regions. - E. Incorrect.
Not partitioning the data relies solely on Databricks' optimizations, which may result in suboptimal performance for large datasets with frequent filtering operations.