Using Amazon AppFlow with Lookout for Metrics - Amazon Lookout for Metrics

Using Amazon AppFlow with Lookout for Metrics

You can use Amazon AppFlow to create a datasource for an Amazon Lookout for Metrics detector. With Amazon AppFlow, you can collect data from external cloud applications in a flow. A Lookout for Metrics detector reads data from the flow and monitors it for anomalies.

To use Amazon AppFlow with Lookout for Metrics, you must use an AWS Key Management Service (AWS KMS) encryption key that you create in your account. You can create a symmetric customer-managed key for your flow and detector in the AWS KMS console.

Lookout for Metrics supports the following cloud applications with Amazon AppFlow:

  • Salesforce

  • Marketo

  • Dynatrace

  • Singular

  • Zendesk

  • Servicenow

  • Infor Nexus

  • Trendmicro

  • Veeva

  • Google Analytics

  • Amplitude

To create a flow

  1. Open the Amazon AppFlow console.

  2. Choose Create flow.

  3. Enter a name for the flow.

  4. For Data encryption choose Customize encryption settings.

  5. For Source details, choose a datasource.

  6. For Destination details, choose Lookout for Metrics.

  7. For Schedule frequency, choose a frequency that matches your detector's interval.

  8. Follow the instructions to configure the remaining options and then choose Next.

  9. For Field mapping, you can choose Map all fields directly, or choose a subset of fields that contains the measures, dimensions, and timestamps that you will use with Lookout for Metrics.

  10. Follow the instructions to complete flow setup and then choose Create flow.

To create an Amazon AppFlow dataset

  1. Open the Lookout for Metrics console Detectors page.

  2. Choose a detector.

  3. Choose Add dataset.

  4. Choose the cloud application that you chose for your flow.

  5. Follow the instructions to create the datasource.

With Amazon AppFlow, you choose fields from the flow's data to be measures and dimensions. The detector monitors the measures for anomalies within values of the dimensions. For example, if you have a measure named availability and a dimension named region, the detector finds anomalies of availability in each region.

The detector reads new data from Amazon AppFlow periodically, by accessing the flow. If it detects any anomalies in the metrics for the interval, it records an anomaly and sends anomaly alerts, if configured.

When you activate the detector, it uses data from several intervals to learn, before attempting to find anomalies. For a five minute interval, the training process takes approximately one day. Training time varies depending on the detector's interval.

Note

When you add an Amazon AppFlow dataset to your detector, the Lookout for Metrics console creates a service role with permission to read from the flow.

For more information about Amazon AppFlow, see Getting started with Amazon AppFlow in the Amazon AppFlow User Guide.