DEA-C01 Question 296
Select 2A company is implementing a real-time analytics pipeline where data from IoT devices is ingested into AWS. The data should be processed, transformed, and stored in a data warehouse for reporting purposes. The company also needs to ensure that the solution can handle spikes in traffic and maintain low latency. Which combination of services provides the most efficient and scalable solution?
- A
Amazon Kinesis Data Streams for ingestion, AWS Lambda for processing, and Amazon Redshift for storage
- B
Amazon S3 for ingestion, AWS Glue for processing, and Amazon RDS for storage
- C
Amazon Kinesis Data Firehose for ingestion and delivery, AWS Lambda for processing, and Amazon Redshift for storage
- D
Amazon SQS for ingestion, AWS Step Functions for processing, and Amazon DynamoDB for storage
- E
Amazon Managed Streaming for Apache Kafka (MSK) for ingestion, AWS Glue for processing, and Amazon Athena for querying
Show answer and explanation
Correct answers: A, C
Explanation
To build a scalable and efficient real-time analytics pipeline, Amazon Kinesis Data Streams or Kinesis Data Firehose are ideal for data ingestion due to their ability to handle high-throughput, low-latency data streams. AWS Lambda is well-suited for real-time processing, and Amazon Redshift provides a powerful data warehouse solution for analytics and reporting. These services together ensure scalability, low latency, and efficient processing.
- A. Correct.
This is a correct answer. Amazon Kinesis Data Streams can handle high-throughput, low-latency data ingestion, while AWS Lambda can process the data in real-time, and Amazon Redshift is suitable for analytics and reporting at scale.
- B. Incorrect.
This is not correct. Amazon S3 is more suitable for batch ingestion rather than real-time ingestion, and RDS is not designed to handle the analytics workload as efficiently as Amazon Redshift.
- C. Correct.
This is a correct answer. Amazon Kinesis Data Firehose handles real-time data ingestion and delivery to destinations like Amazon Redshift, while AWS Lambda can process the data as it flows through the pipeline.
- D. Incorrect.
This is not correct. Amazon SQS is a message queue and is not designed for real-time data streams, and Amazon DynamoDB is not suitable for analytics workloads like data warehousing.
- E. Incorrect.
This is not correct. While Amazon MSK and AWS Glue can process data, Amazon Athena is a query service and does not provide the persistent storage or optimized analytics capabilities needed for the scenario.