Databricks Data Engineer Associate Question 81
Select 2You are working on an ELT pipeline using Apache Spark in Databricks. The raw data is stored in a Delta Lake table, and you need to perform transformation steps that include filtering invalid records, aggregating data, and writing the results back to another Delta Lake table. Which sequence of Spark operations would ensure efficient ELT processing?
- A
Use Spark DataFrame transformations like filter() and groupBy() for data processing, and write the results back using the DataFrame write API with Delta format.
- B
Load data into a Pandas DataFrame for processing and write the results back to Delta using the Delta Lake Python API.
- C
Use Spark SQL queries to directly transform the Delta table and write back the results to another Delta table.
- D
Use Spark RDDs for data processing and convert them to Delta tables for writing results.
- E
Leverage Databricks Auto Loader to process the data in real-time before writing it to Delta.
Show answer and explanation
Correct answers: A, C
Explanation
The recommended approaches for performing ELT with Apache Spark in Databricks include using Spark DataFrame transformations or Spark SQL queries, as both are highly optimized for distributed processing and work seamlessly with Delta Lake. Other options, like using Pandas DataFrames or RDDs, are not well-suited for this scenario, and Databricks Auto Loader is intended for real-time data ingestion rather than ELT transformations.
- A. Correct.
This is a correct approach as Spark DataFrame transformations are optimized for distributed processing, and using the write API with Delta ensures efficient storage and transactional support.
- B. Incorrect.
This is incorrect as Pandas DataFrames are not optimized for distributed processing in large-scale datasets, and this approach would not be efficient in a Spark-based ELT pipeline.
- C. Correct.
This is a correct approach because Spark SQL allows for direct query-based transformations on Delta tables, which are highly optimized for performance.
- D. Incorrect.
This is incorrect because RDDs are a lower-level abstraction than DataFrames and are not recommended for modern ELT pipelines due to their lack of optimization and ease of use.
- E. Incorrect.
This is incorrect because Databricks Auto Loader is designed for real-time ingestion of streaming data, not for transforming and writing data already stored in Delta tables.