What is the MOST efficient way to accomplish these tasks?
Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection. Then use Kinesis Data Firehose to stream the results to Amazon S3.
Ingest the data into Apache Spark Streaming using Amazon EMR, and use Spark MLlib with k-means to perform anomaly detection. Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake.
Ingest the data and store it in Amazon S3. Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.
Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data. Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data.
Explanations:
This option utilizes Amazon Kinesis Data Firehose for real-time data ingestion and Amazon Kinesis Data Analytics with Random Cut Forest (RCF) for anomaly detection, providing an efficient, serverless solution that allows for immediate scoring of malicious events. The results can be streamed directly to Amazon S3 for storage and future analysis, making it a streamlined and effective approach.
While Apache Spark Streaming and Spark MLlib can be used for anomaly detection, this approach introduces additional complexity and overhead with the need to manage an EMR cluster and HDFS. It may also result in higher latency compared to the real-time processing capabilities offered by Kinesis, making it less efficient for the company’s needs.
This option involves ingesting data into Amazon S3 and using AWS Batch to train a k-means model on TensorFlow, which is not a real-time anomaly detection solution. Training a model requires a significant amount of time and resources and does not provide immediate results for scoring incoming data, failing to meet the requirement for real-time analysis.
Although this option proposes using Amazon S3 for data storage and AWS Glue for data transformation, it lacks a real-time anomaly detection mechanism. The built-in RCF model within Amazon SageMaker is not designed for immediate scoring on data as it is ingested, and triggering a job on demand does not support the requirement for real-time event scoring.
I strategize that the answer is:
Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection. Then use Kinesis Data Firehose to stream the results to Amazon S3.