A company needs to develop a system for a hospital. The application needs to ingest the heart rate recorded for various patients. The requirements for the application are:
A data lake that can expand on demand to store the heart rate information.
A way to ingest the data and store it in the data lake.
A way to catalogue the information
Which of the following would you use for this requirement?

Answer options:

A.AWS Redshift as the data lake, AWS SQS to ingest data and AWS Glue to catalogue the information
B.AWS S3 as the data lake, AWS SQS to ingest data and AWS Glue to catalogue the information
C.AWS S3 as the data lake, AWS Kinesis to ingest data and AWS Glue to catalogue the information
D.AWS S3 as the data lake, AWS Kinesis to ingest data and AWS Athena to catalogue the information

Answer – C
An example of this architecture is given in the AWS Documentation
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How to build a front-line concussion monitoring system using AWS IoT and serverless data lakes – Part 2
In part 1 of this series, we demonstrated how to build a data pipeline in support of a data lake. We used key AWS services such as Amazon Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and AWS Lambda. In part 2, we discuss how to process and visualize the data by creating a serverless data lake that uses key analytics to create actionable data.
Create a serverless data lake and explore data using AWS Glue, Amazon Athena, and Amazon QuickSight
As we discussed in part 1, you can store heart rate data in an Amazon S3 bucket using Kinesis Data Streams. However, storing data in a repository is not enough. You also need to be able to catalog and store the associated metadata related to your repository so that you can extract the meaningful pieces for analytics.
For a serverless data lake, you can use AWS Glue, which is a fully managed data catalog and ETL (extract, transform, and load) service. AWS Glue simplifies and automates the difficult and time-consuming tasks of data discovery, conversion, and job scheduling. As you get your AWS Glue Data Catalog data partitioned and compressed for optimal performance, you can use Amazon Athena for the direct query to S3 data. You can then visualize the data using Amazon QuickSight.
The following diagram depicts the data lake that is created in this demonstration:
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Options A and B are incorrect since SQS would not be the ideal service to ingest the information
Option D is incorrect since AWS Athena is a querying tool
For more information on this use case, please visit the url
https://aws.amazon.com/blogs/big-data/how-to-build-a-front-line-concussion-monitoring-system-using-aws-iot-and-serverless-data-lakes-part-2/