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Answer: Connect the IoT sensors to AWS IoT Core. Set a rule to invoke an AWS Lambda function to parse the information and save a .csv file to Amazon. S3 Use AWS Glue to catalog the files. Use Amazon Athena and Amazon QuickSight for analysis.
Option A is the most suitable solution for the following reasons: 1. **AWS IoT Core for Integration**: AWS IoT Core can directly connect the IoT sensors, allowing for continuous streaming of the sensor data to AWS. This ensures real-time or near-real-time data ingestion without requiring changes to the existing sensor code. 2. **AWS Lambda for Parsing**: AWS Lambda provides a serverless environment to handle the variability in the data format by invoking customized logic specific to each vendor. This allows scalable, event-driven processing of data as it arrives, ensuring timely and cost-effective transformations. 3. **Amazon S3 for Storage**: The parsed data can be stored as .csv files in Amazon S3, which is a cost-efficient, scalable, and durable storage solution. This approach offloads the need for managing a traditional relational database and leverages the reliability of S3. 4. **AWS Glue for Data Cataloging**: AWS Glue can automatically discover and catalog the schema of the data stored in S3. This provides an easy and managed way to prepare and catalog the data, making it readily available for querying. 5. **Amazon Athena for Querying**: Amazon Athena enables serverless, ad-hoc querying of the data stored in S3 using standard SQL. It eliminates the need for a complex ETL process and provides fast and scalable query responses. 6. **Amazon QuickSight for Visualization**: Amazon QuickSight can be used for interactive data visualization and business intelligence. It seamlessly integrates with Athena, allowing for real-time analytics without substantial infrastructure overhead. This combination significantly optimizes costs through serverless architecture, improves data ingestion and processing time, and provides powerful analysis and visualization capabilities.
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A company has purchased appliances from different vendors. The appliances all have IoT sensors. The sensors send status information in the vendors' proprietary formats to a legacy application that parses the information into JSON. The parsing is simple, but each vendor has a unique format. Once daily, the application parses all the JSON records and stores the records in a relational database for analysis. The company needs to design a new data analysis solution that can deliver faster and optimize costs. Which solution will meet these requirements?
A
Connect the IoT sensors to AWS IoT Core. Set a rule to invoke an AWS Lambda function to parse the information and save a .csv file to Amazon. S3 Use AWS Glue to catalog the files. Use Amazon Athena and Amazon QuickSight for analysis.
B
Migrate the application server to AWS Fargate, which will receive the information from IoT sensors and parse the information into a relational format. Save the parsed information to Amazon Redshlft for analysis.
C
Create an AWS Transfer for SFTP server. Update the IoT sensor code to send the information as a .csv file through SFTP to the server. Use AWS Glue to catalog the files. Use Amazon Athena for analysis.
D
Use AWS Snowball Edge to collect data from the IoT sensors directly to perform local analysis. Periodically collect the data into Amazon Redshift to perform global analysis.