Exam AWS Certified Data Engineer - Associate DEA-C01 topic 1 question 12 discussion

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

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

A is correct

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]

Firehose cannot use Timestream as destination. Answer is A

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.

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.

Because: The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.

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

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

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

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.

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.

A. - **Processing Sensor Data**: Utilizes Amazon Managed Service for Apache Flink, a fully managed service for real-time data processing. This service is used to process the sensor data, which likely involves real-time analysis or transformation of incoming data streams. - **Connector for Apache Flink to Amazon Timestream**: Integrates a connector for Apache Flink to write processed data into Amazon Timestream, a fully managed time-series database. Timestream is optimized for IoT and time-series data. - **Timestream Database as a Source for Grafana Dashboard**: The data stored in Timestream serves as the data source for creating a Grafana dashboard. Grafana is a popular open-source analytics and monitoring platform that visualizes time-series data.

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.

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.