A company needs to collect streaming data from several sources and store the data in the AWS Cloud. The dataset is heavily structured, but analysts need to perform several complex SQL queries and need

Question

Options

• AUse Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon S3.
• BUse Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon
• CUse Amazon Kinesis Data Firehose to ingest the data to save it to Amazon Redshift. Enable
• DUse Amazon Kinesis Data Firehose to ingest the data to save it to Amazon S3. Load frequently

• A
  9% (4)
• B
  19% (9)
• C
  6% (3)
• D
  66% (31)

Explanation

Option D uses Amazon Kinesis Data Firehose (managed, low-overhead streaming ingest) to land all data in S3, then loads only frequently queried data into Amazon Redshift. This is cost-effective because S3 is cheap for bulk storage and Redshift (with its columnar MPP engine) provides consistent, fast SQL query performance for hot data. Loading only frequently accessed data into Redshift minimizes Redshift storage and compute costs. Options A and B use Amazon MSK (Managed Kafka), which adds significant operational overhead compared to Firehose. Option C sends all data to Redshift via Firehose, which is more expensive than tiering cold data to S3. Storing all data in Redshift is not cost-effective when only a subset is queried frequently.

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