DEA-C01 Question 112
Select 2You are designing a data pipeline in AWS to process streaming data from IoT devices. The data needs to be ingested in real-time, stored for long-term analytics, and queried with low latency. Which combination of AWS services should you use to meet these requirements?
- A
Amazon Kinesis Data Streams for ingestion, Amazon S3 for storage, and Amazon Athena for querying
- B
Amazon Kinesis Data Firehose for ingestion and delivery, Amazon Redshift for storage and querying
- C
Amazon Kinesis Data Streams for ingestion, Amazon DynamoDB for storage, and Amazon EMR for querying
- D
Amazon Managed Streaming for Apache Kafka (MSK) for ingestion, Amazon S3 for storage, and AWS Glue for querying
- E
Amazon Kinesis Data Streams for ingestion, Amazon S3 for storage, and Amazon Redshift Spectrum for querying
Show answer and explanation
Correct answers: A, E
Explanation
To design a pipeline that supports real-time ingestion, cost-effective long-term storage, and low-latency querying, you need to combine services that specialize in each area. Amazon Kinesis Data Streams handles the real-time ingestion of streaming data. Amazon S3 is a scalable and economical option for long-term storage. For querying, Amazon Athena provides serverless querying capabilities on data stored in S3, and Redshift Spectrum allows querying S3 data using the Redshift engine. Both combinations [1] and [5] meet the requirements effectively.
- A. Correct.
This is a correct option because Amazon Kinesis Data Streams can handle real-time ingestion, Amazon S3 is a cost-effective choice for long-term storage, and Amazon Athena can perform ad-hoc queries on data stored in S3.
- B. Incorrect.
This option is incorrect because while Kinesis Data Firehose delivers data to storage destinations, Amazon Redshift is primarily for structured data and is not ideal for querying massive unstructured datasets directly.
- C. Incorrect.
This option is incorrect because Amazon DynamoDB is designed for key-value and document-based storage, not for analytics or long-term storage. Additionally, Amazon EMR is better suited for batch processing, not low-latency querying.
- D. Incorrect.
This option is incorrect because while Amazon MSK can be used for ingestion, AWS Glue is a data integration service and not suitable for low-latency querying.
- E. Correct.
This is a correct option because Amazon Kinesis Data Streams can handle real-time ingestion, Amazon S3 is ideal for storing large amounts of data, and Redshift Spectrum allows querying data directly from S3 for analytics with low latency.