Databricks Data Engineer Associate Question 82
Select 3You are tasked with transforming raw JSON data stored in a Delta table into a cleaned format for downstream analytics. The raw data contains nested structures, and you need to flatten these structures while ensuring the transformations are efficient and scalable. Which of the following steps should you include in your ELT pipeline using Apache Spark?
- A
Use the
explode()function to flatten nested arrays or structs in the JSON data. - B
Use the
selectExpr()function to directly write a SQL query for extracting and transforming specific fields. - C
Use the
pivot()function to aggregate and reshape the nested JSON data. - D
Write the cleaned data back to a Delta table to maintain scalability and support ACID transactions.
- E
Use the
approxQuantile()function to estimate approximate statistics for the nested fields.
Show answer and explanation
Correct answers: A, B, D
Explanation
To efficiently transform nested JSON data in an ELT pipeline using Apache Spark, you should flatten the data using explode() for arrays or structs, extract specific fields using selectExpr() or similar transformations, and write the cleaned data to a Delta table to ensure scalability and reliability. Other functions like pivot() and approxQuantile() are not directly applicable to this scenario.
- A. Correct.
The
explode()function is commonly used to flatten nested arrays or structs, making it easier to work with nested JSON data. - B. Correct.
The
selectExpr()function allows you to use SQL-like syntax to extract and transform specific fields, which is useful for working with structured data like JSON. - C. Incorrect.
The
pivot()function is used for reshaping and aggregating data, but it is not relevant for flattening or cleaning nested JSON structures. - D. Correct.
Writing the cleaned data to a Delta table ensures scalability, ACID compliance, and compatibility with downstream processes.
- E. Incorrect.
The
approxQuantile()function is used for estimating statistics, but it does not help in flattening or cleaning nested JSON data.