You are a member of a machine learning team at a large online retailer. Your team is responsible for retail competitor analysis. You have a competitive product data streaming source that you need to ingest into your data lake. You need to use that streaming competitor product data to match the corresponding product data in your catalog of products. Using this matching, your data scientists can produce competitive analysis dashboards in a BI tool.
Which of the following options gives you the best data ingestion and most efficient product comparison solution?

Answer options:

A.Kinesis Data Streams -> S3 -> Lake Formation -> QuickSight
B.Kinesis Data Streams -> Elasticsearch -> Kibana
C.Kinesis Data Firehose -> S3 -> Lake Formation -> QuickSight
D.Kinesis Data Firehose -> Elasticsearch -> Kibana

Answer: C
Option A is incorrect. Kinesis Data Streams cannot write directly to S3. It needs a Kinesis Consumer Library application to receive the data and then write it to S3.
Option B is incorrect. Kinesis Data Streams cannot write directly to Elasticsearch. Also, Elasticsearch has no built-in data matching capability.
Option C is CORRECT. Kinesis Data Firehose can stream directly to S3. Lake Formation has a FindMatches transform that enables you to identify matching records in your dataset, even when the records do not have a common unique identifier. No fields match exactly. In this scenario, you will match products in your product catalog with your competitive product sources even though the product entries are structured differently. QuickSight allows your data scientists to produce their analysis dashboards.
Option D is incorrect. Kinesis Data Firehose can write directly to Elasticsearch. But Elasticsearch has no built-in data matching capability.
Reference:
Please see the AWS Lake Formation developer guide titled What Is AWS Lake Formation?.
Please see the AWS Glue developer guide titled Matching Records with AWS Lake Formation FindMatches.