Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data to save it to Amazon S3. Use Amazon Athena to perform SQL queries over the ingested data.
B.
Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads.
C.
Use Amazon Data Firehose to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads.
D.
Use Amazon Data Firehose to ingest the data to save it to Amazon S3. Load frequently queried data to Amazon Redshift using the COPY command. Use Amazon Redshift Spectrum for less frequently queried data.
The Answer Is:
D
This question includes an explanation.
Explanation:
The most cost-effective way to provide consistent SQL query performance on a heavily structured dataset, where some data is accessed more frequently, is to use Amazon Redshift as your main data warehouse for hot data and Amazon Redshift Spectrum to query cold data that remains in S3. With this approach, frequently queried data is loaded into Redshift for fast, consistent querying, while infrequently accessed data is left in S3 and accessed on demand via Spectrum, avoiding unnecessary data warehousing costs. Amazon Kinesis Data Firehose provides an easy and scalable way to ingest streaming data directly to both S3 and Redshift.
AWS Documentation Extract:
"Amazon Redshift Spectrum allows you to run queries against exabytes of data in Amazon S3 without loading or transforming the data. Load hot data into Redshift for fast access and query cold data in S3 with Spectrum, optimizing both cost and performance."
(Source: Amazon Redshift documentation, Spectrum)
A: Athena is good for ad hoc queries but not for consistent, high-performance SQL workloads.
B, C: Loading all data into Redshift is not cost-effective if some data is infrequently accessed.
[Reference: AWS Certified Solutions Architect – Official Study Guide, Data Warehousing and Analytics., ]
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