Low Latency Real-Time Sensor Dashboard with Kinesis and Timestream

Automate data processing by using AWS services.

Answer Correct answer: A — Use Amazon Managed Service for Apache Flink to process data into Amazon Timestream and visualize it with a Grafana dashboard.

A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket. The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility. Which solution will meet these requirements with the LOWEST latency?

Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. Correct Answer
Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.
Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.
Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

Community Votes

100%

100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

The exam tests the distinction between near-real-time BI tools like QuickSight and true low-latency streaming visualizations like Grafana, alongside valid service integrations.

This question evaluates the selection of AWS services for a real-time operational dashboard requiring the lowest possible latency from sensor data ingestion to visualization.

Many candidates choose Option C because it uses an AWS-native stack (QuickSight) and assumes Firehose supports Timestream, but Firehose does not natively support Amazon Timestream as a destination, and QuickSight has higher latency than Grafana.

Community Discussion (15 comments)

fceb2c1 👍 12 Selected: A

https://aws.amazon.com/blogs/database/near-real-time-processing-with-amazon-kinesis-amazon-timestream-and-grafana/ Look at the architecture diagram

milofficial 👍 7 Selected: A

real time -> no Quicksight. And bookmarks to read sensor data real time is just as stupid as the flat earth theory. A it is.

Salmanbutt786 👍 1 Selected: A

A is correct C is close to A, but creating an additional Data Firehose delivery stream adds unnecessary complexity. Writing directly to Amazon Timestream from Apache Flink, as in option A, is more straightforward and ensures lower latency.

Scotty_Nguyen 👍 1 Selected: A

A is correct

Adrifersilva 👍 3 Selected: A

Grafana: Real-time Performance: Grafana is known for its excellent real-time data visualization capabilities. It's often used for operational dashboards that require frequent updates. Integration: Works well with time-series databases and streaming data sources. [2]

deepcloud 👍 5 Selected: A

Firehose cannot use Timestream as destination. Answer is A

samadal 👍 1

Option A is for processing data in Flink and then sending it to Timestream. This is advantageous when complex data processing is required in Flink, but the processing step where complex analytics are processed can handle additional latency. Option C performs data processing in Flink, sends the data directly to Timestream without any additional steps, and provides dashboards via QuickSight. Since data can be started immediately after arriving in Timestream, latency is likely to be higher. Therefore, option C is preferable because it can handle latency by performing data processing, publishing data directly to Timestream, and provides fast dashboards using QuickSight.

teo2157 👍 4 Selected: A

Amazon QuickSight is primarily designed for business intelligence and data visualization, and it can provide near real-time views depending on the data refresh rate. However, it is not typically used for real-time streaming data visualization with very low latency. For real-time dashboards with very low latency, services like Grafana are more suitable. You can use Amazon Managed Grafana to setup the dashboard so you're using an AWS service which is always preferible on these exams.

jyrajan69 👍 2

Based on this it should be C, why use an open source app when you can an AWS Service https://community.amazonquicksight.com/t/real-time-data-visualization-capabilities-of-amazon-quicksight/24007

Just_Ninja 👍 1

The Question is: Which solution will meet these requirements with the LOWEST latency? So just A can be the right answer "lowest latency!!!!"

LanoraMoe 👍 1

I go with Option A. Kinesis Data Firehose can connect to 3 AWS destinations so far S3, Redshift and OpenSearch.

certplan 👍 1

Option A: - Involves additional steps: Option A requires writing data to Amazon Timestream after processing with Apache Flink, potentially introducing additional latency compared to a more direct approach like Option C. - Grafana integration: While Grafana is a powerful visualization tool, setting up and configuring Grafana dashboards might require additional effort compared to using Amazon QuickSight, which offers more straightforward integration with AWS services like Amazon Timestream.

certplan 👍 2

C. - Processing Sensor Data with Amazon Flink: Similar to option A, this approach uses Amazon Managed Service for Apache Flink to process sensor data, providing real-time analytics or transformation capabilities. - Data Firehose Delivery Stream to Timestream: Sets up a new Amazon Data Firehose delivery stream to publish processed data directly to Amazon Timestream. Data Firehose is a fully managed service for delivering real-time streaming data to destinations such as data lakes, databases, and analytics services. - Timestream Database as a Source for QuickSight Dashboard: Similar to option B, the data stored in Amazon Timestream serves as the data source for creating an Amazon QuickSight dashboard.

certplan 👍 1

Considerations: Option A utilizes Amazon Managed Service for Apache Flink to process sensor data and then writes the processed data to Amazon Timestream. From there, the Timestream database serves as a source to create a Grafana dashboard. Thus the data goes through Apache Flink for processing, then to Timestream, and finally to Grafana. "Each additional step introduces potential latency". Option C processes sensor data using Amazon Managed Service for Apache Flink and then publishes data directly to Amazon Timestream via a Data Firehose delivery stream. Finally, it uses Timestream as a source to create an Amazon QuickSight dashboard. So, in terms of latency, both options involve processing data in real-time using Apache Flink. However, Option C has a more direct data flow by publishing data directly to Timestream, potentially reducing latency compared to Option A, where the data has to go through an additional step of writing to Timestream.

TonyStark0122 👍 2

A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. Explanation: Amazon Managed Service for Apache Flink provides real-time stream processing capabilities, which can process sensor data with low latency. By using Apache Flink connectors, the processed data can be efficiently written to Amazon Timestream, which is optimized for time-series data storage and querying.

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Expert Analysis

Why the Answer Is Correct

Option A is the correct solution because it leverages Amazon Managed Service for Apache Flink for low-latency stream processing and connects directly to Amazon Timestream, a purpose-built time-series database. Grafana is the industry standard for real-time operational dashboards with sub-second refresh capabilities, making it superior to QuickSight for this specific 'large screen' factory floor requirement. The architecture described in Option A aligns with AWS best practices for high-performance IoT telemetry visualization.

Why the Other Options Are Wrong

Option B relies on S3 notifications and Lambda, which introduces significant latency due to object storage write cycles and cold starts, failing the 'real-time' requirement. Option C is incorrect because Amazon Kinesis Data Firehose does not support Amazon Timestream as a native delivery destination; furthermore, Amazon QuickSight is optimized for business intelligence with scheduled or near-real-time refreshes, not the ultra-low latency required for live factory monitoring. Option D suggests using AWS Glue bookmarks to read from S3 in real time, which is technically invalid as Glue is primarily for batch/ETL workloads and reading from S3 inherently lacks the streaming low-latency characteristics of Kinesis.

Community Comment Notes

Community consensus strongly favors Option A, with users highlighting that "Grafana is known for its excellent real-time data visualization capabilities" compared to QuickSight. Several commenters correctly identified that "Firehose cannot use Timestream as destination," ruling out Option C. One user noted the architectural validity by referencing an AWS blog post demonstrating "near-real-time processing with Amazon Kinesis, Amazon Timestream, and Grafana."

Official Reference

https://aws.amazon.com/blogs/database/near-real-time-processing-with-amazon-kinesis-amazon-timestream-and-grafana/

Exam Strategy

Always distinguish between 'Business Intelligence' tools (QuickSight, Redshift) and 'Operational Monitoring' tools (Grafana, CloudWatch). For questions emphasizing 'lowest latency' and 'real-time' on factory floors, prioritize streaming databases (Timestream, DynamoDB) paired with visualization tools designed for continuous updates.

Frequently Asked Questions

Why is QuickSight not suitable for this low-latency scenario?

QuickSight is designed for BI and typically has refresh intervals ranging from minutes to seconds, whereas Grafana can query time-series databases like Timestream for sub-second real-time updates.

Can Kinesis Data Firehose write directly to Amazon Timestream?

No, Kinesis Data Firehose does not support Amazon Timestream as a native destination. You must use a consumer application like Apache Flink or Kinesis Client Library to ingest and write to Timestream.

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