Question #972
An air quality monitoring network has deployed over 10,000 environmental sensors that continuously transmit air quality metrics. Each update is under 1 MB in size. The network uses on-premises application servers to process incoming data, convert it into a structured format, and store it in an on-premises relational database. Data analysts use SQL queries to analyze the data. Maintenance tasks on the servers, such as updates and patches, cause downtime and data loss during peak loads. The organization wants to minimize operational overhead, reduce maintenance efforts, and ensure high availability. A solutions architect proposes using AWS IoT Core for data ingestion. What additional solution should the architect recommend?
Route sensor data to Amazon Kinesis Data Firehose. Use an AWS Lambda function to process the data into .csv format and insert it into an Amazon Aurora MySQL DB instance. Instruct analysts to query the database directly.
Route sensor data to Amazon Kinesis Data Firehose. Use an AWS Lambda function to process the data into Apache Parquet format and store it in an Amazon S3 bucket. Instruct analysts to query the data using Amazon Athena.
Route sensor data to an Amazon Managed Service for Apache Flink application to convert it into .csv format and store it in an Amazon S3 bucket. Import the data into an Amazon Aurora MySQL DB instance. Instruct analysts to query the database directly.
Route sensor data to an Amazon Managed Service for Apache Flink application to convert it into Apache Parquet format and store it in an Amazon S3 bucket. Instruct analysts to query the data using Amazon Athena.
Explanation
Option B is correct because:
1. Serverless Architecture: Kinesis Data Firehose and Lambda eliminate server maintenance, reducing operational overhead.
2. Efficient Storage: Apache Parquet format optimizes storage and query performance for analytics.
3. Scalability & Availability: S3 provides durable, highly available storage, while Athena enables serverless SQL queries without downtime.
Other options are incorrect because:
- A uses Aurora MySQL, which requires maintenance and lacks scalability.
- C introduces unnecessary complexity with Managed Flink and Aurora, failing to reduce operational effort.
- D uses Managed Flink, which is overkill for simple data conversion, increasing operational complexity.
Key Points: Prioritize serverless services (Lambda, Firehose, Athena) and columnar formats (Parquet) for scalable, low-maintenance analytics solutions.
Answer
The correct answer is: B