Databricks Certified Data Engineer - Associate
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In a data engineering project, you are working with a Delta Lake table named 'employee_data' that contains columns 'employee_id', 'first_name', 'last_name', and 'salary'. Due to a data ingestion error, there are duplicate entries based on the 'employee_id' column. Your task is to deduplicate the data efficiently while ensuring the solution is scalable and maintains data integrity. Considering the need for a solution that is both performant and easy to maintain, which of the following Spark SQL queries would you use to deduplicate the 'employee_data' table based on the 'employee_id' column? Choose the best option from the following:
In a data engineering project, you are working with a Delta Lake table named 'employee_data' that contains columns 'employee_id', 'first_name', 'last_name', and 'salary'. Due to a data ingestion error, there are duplicate entries based on the 'employee_id' column. Your task is to deduplicate the data efficiently while ensuring the solution is scalable and maintains data integrity. Considering the need for a solution that is both performant and easy to maintain, which of the following Spark SQL queries would you use to deduplicate the 'employee_data' table based on the 'employee_id' column? Choose the best option from the following:
Explanation:
Option D is the correct choice because it utilizes the ROW_NUMBER() window function to efficiently identify and remove duplicate rows based on the 'employee_id' column. This approach is scalable, maintains data integrity, and is performant as it processes the data in a single pass. The query partitions the data by 'employee_id', assigns a unique row number to each row within the partition, and then filters to keep only the first occurrence of each 'employee_id'. This method is recommended for deduplication tasks in Spark SQL due to its effectiveness and efficiency.