MLA-C01 Question 72
Select 3You are designing a machine learning pipeline to process and analyze real-time clickstream data from a website. The data needs to be cleaned, transformed, and enriched in real-time before being fed into a machine learning model for predictions. Which combination of AWS services would be most appropriate for transforming the streaming data in this scenario?
- A
AWS Lambda
- B
Amazon Kinesis Data Analytics
- C
AWS Glue
- D
Amazon SageMaker
- E
Apache Spark on Amazon EMR
Show answer and explanation
Correct answers: A, B, E
Explanation
To process and transform streaming data in real-time, a combination of AWS Lambda, Amazon Kinesis Data Analytics, and Apache Spark on Amazon EMR is most suitable. AWS Lambda can perform lightweight, event-driven transformations, while Kinesis Data Analytics provides SQL-based real-time analytics capabilities. For more complex transformations, Apache Spark on Amazon EMR is a robust option. AWS Glue and Amazon SageMaker are not suited for real-time transformations in this context as Glue is focused on batch processing and SageMaker is primarily for training and deploying machine learning models.
- A. Correct.
AWS Lambda is a serverless compute service that can process data streams in real-time. It is effective for lightweight transformations and enrichment of streaming data.
- B. Correct.
Amazon Kinesis Data Analytics is specifically designed for real-time analytics and transformations of streaming data using SQL queries.
- C. Incorrect.
AWS Glue is a fully managed ETL service, but it is primarily designed for batch data processing, not real-time streaming data processing.
- D. Incorrect.
Amazon SageMaker is a managed machine learning service, but it does not directly handle real-time streaming data transformations.
- E. Correct.
Apache Spark on Amazon EMR is a distributed data processing framework that can be used for complex transformations and enrichments of real-time streaming data when integrated with streaming frameworks like Apache Kafka or Amazon Kinesis.