1. Anomaly Detection Alerts in Streaming Data with AWS SNS


    To create anomaly detection alerts for streaming data in AWS, we will set up a system that involves several AWS services working together. We'll use Amazon Kinesis for streaming data, Amazon CloudWatch for monitoring and anomaly detection, and Amazon Simple Notification Service (SNS) to send notifications when anomalies are detected.

    Here is a step-by-step explanation of how we'll implement the solution:

    1. Amazon Kinesis: This service allows you to intake real-time data such as logs, events, and metrics seamlessly. In this context, Kinesis Streams will be the backbone of our real-time data pipeline.

    2. Amazon CloudWatch: Specifically, we'll use CloudWatch Alarms, which can be set up to watch the metrics coming from Kinesis and identify when data points fall outside of predefined thresholds, which would be our conditions for an anomaly.

    3. Amazon SNS (Simple Notification Service): When an alarm is triggered, we want to notify someone or trigger another process to take an action. This is where SNS comes in; it's a service for managing, sending, and receiving notifications. We'll create an SNS topic that will be the destination of these notifications.

    4. CloudWatch Events (referred to as EventBridge now): To capture the state changes of a CloudWatch alarm (like when it goes into ALARM state), we'll use CloudWatch Events. These events will then be mapped to targets like an SNS topic, to kick off notifications.

    Let's now set up this flow in Pulumi. We will write a Pulumi program that creates a Kinesis stream, sets up CloudWatch for anomaly detection on our stream, and sends an alert using SNS when an anomaly is detected.

    import pulumi import pulumi_aws as aws # Create a Kinesis stream to intake streaming data. kinesis_stream = aws.kinesis.Stream("anomalyDetectionStream") # Create an SNS topic that will receive notifications when anomalies are detected. sns_topic = aws.sns.Topic("anomalyDetectionTopic") # Create a CloudWatch Metric Alarm on the Kinesis stream to detect anomalies. # We have to define what we consider an anomaly in our context by specifying the metric and threshold. metric_alarm = aws.cloudwatch.MetricAlarm("anomalyDetectionAlarm", alarm_description="Alarm when server CPU utilization exceeds 70%", comparison_operator="GreaterThanThreshold", evaluation_periods=1, metric_name="CPUUtilization", namespace="AWS/EC2", # Example namespace, replace with actual one for Kinesis period=60, statistic="Average", threshold=70, alarm_actions=[sns_topic.arn], # Alert will be sent to SNS topic dimensions={ "StreamName": kinesis_stream.name, }, ) # Note: The metric details need to be aligned with the real Kinesis stream metrics. The above example uses CPU Utilization # as an example threshold, which you would likely replace with appropriate Kinesis metrics like 'IncomingRecords' or similar, # based on your anomaly detection requirements. # Export the SNS topic ARN and Kinesis stream name as stack outputs for access outside of Pulumi. pulumi.export("sns_topic_arn", sns_topic.arn) pulumi.export("kinesis_stream_name", kinesis_stream.name)

    This Pulumi program:

    • Defines a Kinesis stream for your streaming data.
    • Sets up a SNS topic that will be used to deliver notifications.
    • Configures CloudWatch to monitor a metric (CPU utilization is used as an example here; in a real-world scenario, you'd monitor a different metric related to your streaming data).
    • Triggers an alarm if the threshold is breached, which will publish a message to the SNS topic.
    • Exports the ARN (Amazon Resource Name) of SNS topic and the name of the Kinesis stream for reference in other services or applications that need it.

    To run this Pulumi program, you need to install the Pulumi CLI, authenticate with AWS using the AWS CLI, and then navigate to the directory containing the Pulumi program file (__main__.py by default) and run pulumi up. This will provision the resources described above in your AWS account.