DEA-C01 Question 106
Single answerYou are working on a data engineering project where you need to process a large amount of data stored in Amazon S3. The processing involves filtering records, transforming specific fields, and aggregating data. You decide to use AWS Glue ETL for this task. Which programming concept should you apply to efficiently distribute and parallelize the data processing across multiple nodes in the Glue job?
- A
MapReduce
- B
Recursion
- C
Threading
- D
Event-driven programming
Show answer and explanation
Correct answer: A
Explanation
AWS Glue uses Apache Spark under the hood, which is based on the MapReduce programming paradigm. This concept allows distributed data processing by dividing the workload across multiple nodes, ensuring scalability and efficiency for large datasets stored in S3. Understanding and applying MapReduce principles is essential for optimizing Glue ETL jobs.
- A. Correct.
MapReduce is the appropriate programming concept here as it allows you to efficiently distribute and parallelize large-scale data processing tasks across multiple nodes, which aligns with the functionality of AWS Glue.
- B. Incorrect.
Recursion is not applicable in this scenario as it is a method of solving problems by breaking them into smaller sub-problems, but it is not designed for distributed parallel processing of large datasets.
- C. Incorrect.
Threading is a low-level concept for parallelism and concurrency, but it is not relevant in a distributed computing environment like AWS Glue, which operates at a higher abstraction level using Spark.
- D. Incorrect.
Event-driven programming focuses on responding to events or triggers, but it does not address the need for parallelizing data processing in distributed systems.