A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake. The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of: Real-time analytics Interactive analytics of historical data Clickstream analytics Product recommendations Which services should the Specialist use?
A) AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
B) Amazon Athena as the data catalog: Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-real-time data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
C) AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
D) Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

Question

Quizplus · Accepted Answer

AWS Glue can be used as a data catalog for organizing and managing data in the data lake. Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics are suitable for real-time data insights. Amazon Kinesis Data Firehose can deliver data to Amazon Elasticsearch Service (ES) for clickstream analytics. Amazon EMR is suitable for generating personalized product recommendations using big data processing frameworks.

A Machine Learning Specialist Working for an Online Fashion Company