A bicycle sharing company is developing a multi-tier architecture to track the location of its bicycles during peak operating hours. The company wants to use these data points in its existing analytics platform. A solutions architect must determine the most viable multi-tier option to support this architecture. The data points must be accessible from the REST API.
Which action meets these requirements for storing and retrieving location data?
The most viable multi-tier option to support the architecture of tracking bicycle locations during peak hours and making them accessible via a REST API would be to use Amazon API Gateway with AWS Lambda. API Gateway can handle the REST API requests while AWS Lambda can process the incoming data points. Although Lambda itself does not store data, it can interface with various storage solutions, allowing the data to be stored in the existing analytics platform mentioned in the question. This setup ensures that data points are processed in real-time and made accessible through the REST API, which satisfies the requirements.
API Gateway is needed to get the data so option A and C are out. “The company wants to use these data points in its existing analytics platform” so there is no need to add Kynesis. Option D is also out. This leaves us with option B as the correct one.
AWS Lambda is a serverless compute service that can be used to run code in response to specific events, such as changes to data in an Amazon S3 bucket or updates to a DynamoDB table. It could be used to process the location data, but it doesn't provide storage solution. Therefore, it would not be the best option for storing and retrieving location data in this scenario.
i dont understand the use of a lambda function here, maybe if there would be need to transform the data, can you explain?
You are right, but it clearly say "store data". AWS Lambda don't store data, Kinesis could.
B might work but D works better. B requieres API gateway + lambda for data input & output, whereas D is a broader solution, as Kinesis Data Analytics APIs can be used to extract and process data better that API Gateway + Lambdas. Also, Kinesis is highly recommended for telemetry data which is the question scenario. @See Kinesys flexible API (https://aws.amazon.com/documentation-overview/kinesis-data-analytics/)
Also by using kinesis the analytics platform will have a storing buffer to take & process data through the kinesys API. The lambda aproach in the B scenario is to wide and leaves many loose ends.
I dont understand why you will vote B? how are you going to store data with just lambda? > Which action meets these requirements for storing and retrieving location data In this use case there will obviously be a ton of data and you want to get real-time location data of the bicycles, and to analyze all these info kinesis is the one that makes most sense here.
https://aws.amazon.com/blogs/aws/real-time-hotspot-detection-in-amazon-kinesis-analytics/
I don't think you need to worry about storing data. The question states their is an existing platform.
Real-time analytics on Kinesis Data Streams & Firehose using SQL, not store db ...
Lambda isn't storing the data themselves. It's triggering the data store to the company's "existing data analytics platform"
Kinesis Data Analytics requires a Kinesis Data Streams or Kinesis Data Firehose as input streams, so you cannot directly connect it to API Gateway, therefore B is the answer
Keyword: "Accessible from the REST API". So option A and C are not correct Option. Remaining Options B and D. Option B is not a correct answer because After API Gateway to Lambda Function, what next ?. where will data go ?. but with Option D API Gateway with Kenesis Data Stream you have an option of sending data(through Firehose) to third party partner destination like DataDog, NewRelic, MongoDB, Splunk etc. Correct Answer D
Comparison to other options: A. Athena and S3: While storing data in S3 and querying it with Athena is cost-effective, it wouldn't provide real-time data access needed for the API. C. QuickSight and Redshift: QuickSight and Redshift are excellent for analytics, but wouldn't directly expose data through the API. You'd still need a Lambda function to act as an intermediary. D. API Gateway and Kinesis Data Analytics: Kinesis Data Analytics is suitable for real-time streaming analysis, but it wouldn't directly provide a REST API endpoint. You'd need additional setup like API Gateway or Lambda for API access.
The company wants to use these data points in its existing analytics platform We don't need analytics platform, B. The correct answer
We need API Gateway for rest apis, so A and C are out. Among B and D, there is an existing analytics platform so no need for Kinesis. Also, Kinesis Data Analytics relies on data source: either Kinesis Data Firehose or Kinesis Data Streams. It cannot work directly with API Gateway, so option D) is out. That leaves B).
Option A makes more sense as this provide both storing option as S3 and rest endpoint in Athena to retrieve data points integrating with S3
B for sure
you can't store data in lambda
Track location, it should be streaming
D is more reasonable than B
https://docs.aws.amazon.com/lambda/latest/dg/services-apigateway.html
Amazon API Gateway with Amazon Kinesis Data Analytics is used for processing and analyzing streaming data in real-time using SQL or Apache Flink. While it's suitable for real-time analytics, it's not typically used for storing and retrieving data in a RESTful API format.
B. The q states that they already have an analytics platform, I thought D but that would be changing the platforms.
selected answer: D kinesis can store the data for analysis.
D because B mentions no way to store data.