What approach would be the MOST effective to perform near-real time defect detection?
Use AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from within AWS IoT Analytics to carry out analysis for anomalies.
Use Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry out Apache Spark ML k-means clustering to determine anomalies.
Use Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random Cut Forest (RCF) algorithm to determine anomalies.
Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.
Explanations:
AWS IoT Analytics is suitable for IoT data analysis but lacks the capability for real-time anomaly detection. While Jupyter notebooks can be used for analysis, they are not optimized for real-time processing, making this option less effective for immediate defect detection.
While Amazon S3 is good for storage and Amazon EMR with Apache Spark can analyze large datasets, this approach does not provide real-time capabilities for defect detection. The batch processing nature of EMR means it is not ideal for near-real-time analysis needed for immediate defect detection.
This option uses Amazon S3 for storage and Amazon SageMaker’s Random Cut Forest for anomaly detection. However, SageMaker is not inherently designed for near-real-time processing. It is better suited for batch processing and offline analysis, making it less effective for immediate defect detection in a real-time context.
Amazon Kinesis Data Firehose is designed for real-time data ingestion and can stream data to other services. Using Kinesis Data Analytics with the Random Cut Forest algorithm allows for near-real-time anomaly detection, while also enabling data storage in Amazon S3 for further offline analysis, making this the most effective option.
My best guess is:
Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.