Amazon Kinesis Data Analytics | Configuring Destinations

Question 1

How can I perform real-time anomaly detection in Kinesis Data Analytics?

Configuring Destinations

Amazon Kinesis Data Analytics | Analytics

Answer 1

Kinesis Data Analytics includes pre-built SQL functions for several advanced analytics including one for anomaly detection. You can simply make a call to this function from your SQL code for detecting anomalies in real-time. Kinesis Data Analytics uses the Random Cut Forest algorithm to implement anomaly detection. For more information on Random Cut Forests, see the Streaming Data Anomaly Detection whitepaper.

Question 2

How do I set up a destination?

Configuring Destinations

Amazon Kinesis Data Analytics | Analytics

Answer 2

In your application code, you write the output of SQL statements to one or more in-application streams. Optionally, you can add an output configuration to your application to persist everything written to specific in-application streams to up to four external destinations. These external destinations can be an Amazon S3 bucket, Amazon Redshift table, Amazon Elasticsearch domain (through Amazon Kinesis Data Firehose) and an Amazon Kinesis data stream. Each application supports up to four destinations, which can be any combination of the above. For more information, see Configuring Output Streams in the Kinesis Data Analytics Developer Guide.

Question 3

My preferred destination is not directly supported. How can I send SQL results to this destination?

Configuring Destinations

Amazon Kinesis Data Analytics | Analytics

Answer 3

You can use AWS Lambda to write to a destination that is not directly supported using. We recommend that you write results to an Amazon Kinesis data stream, and then use AWS Lambda to read the processed results and send it to the destination of your choice. For more information, see the Example: AWS Lambda Integration in the Kinesis Data Analytics Developer Guide. Alternatively, you can use an Amazon Kinesis Data Firehose delivery stream to load the data into Amazon S3, and then trigger an AWS Lambda function to read that data and send it to the destination of your choice. For more information, see Using AWS Lambda with Amazon S3 in the AWS Lambda Developer Guide.