Question

A manufacturing company launched a new type of IoT sensor. The sensor will be used to collect large streams of data records. You need to create a solution that can ingest and analyze the data in real-time with millisecond response times. Which of the following is the best option that you should implement in this scenario?

a. Ingest the data using Amazon Kinesis Data Streams and create an AWS Lambda function to store the data in Amazon DynamoDB.
b. Ingest the data using Amazon Kinesis Data Streams and create an AWS Lambda function to store the data in Amazon Redshift
c. Ingest the data using Amazon Kinesis Data Firehose and create an AWS Lambda function to store the data in Amazon DynamoDB
d. Ingest the data using Amazon Simple Queue Service and create an AWS Lambda function to store the data in Amazon Redshift.

Answer 1

The best option is to ingest the data using Amazon Kinesis Data Streams and store it in Amazon DynamoDB using an AWS Lambda function.

Step-by-step explanation:

The best option for ingesting and analyzing the large streams of data in real-time with millisecond response times in this scenario is to ingest the data using Amazon Kinesis Data Streams and create an AWS Lambda function to store the data in Amazon DynamoDB (option a).

Amazon Kinesis Data Streams is a fully managed service for ingesting real-time streaming data at a large scale. AWS Lambda is a serverless compute service that allows running code without provisioning or managing servers, making it suitable for real-time processing of data.

Amazon DynamoDB is a fast and scalable NoSQL database that can handle large amounts of data with low latency. By combining these services, you can achieve the desired goal of ingesting and analyzing the data in real-time with millisecond response times.