Datadog

this guide explains how to connect resolve ai with datadog resolve uses this connection to ingest metrics, logs, dashboards, and traces for alert investigations 1\ create tokens in datadog, create both an https //app datadoghq com/organization settings/api keys and https //app datadoghq com/organization settings/application keys on each page click + new key name it resolve and click create key click copy and save the key to use below app key scopes if you use https //docs datadoghq com/account management/api app keys/#scopes for granular permissions, ensure you include the minimum requirements apm apm api catalog read, apm pipelines read, apm read, apm service catalog read dashboards dashboards read events events read logs logs read data, logs read index data metrics metrics read, timeseries query monitors monitors read rum rum apps read slos slos read usage usage read service accounts if you use datadog service accounts to manage app keys, create a service account with a role with the permissions listed under "app key scopes" then, create an app key for that service account 2\ connect datadog set up the integration either in the resolve ui or in the satellite configuration connect via resolve ui open https //app0 resolve ai/integrations/datadog/connect in resolve click add connection enter a name (ex datadog main ) leave environments blank to ingest data from all environments, or list specific ones that match your satellite config paste your https //app datadoghq com/organization settings/api keys and https //app datadoghq com/organization settings/application keys from datadog enter your site domain (ex datadoghq com ) (optional) enable https //docs resolve ai/sensitive data redaction turn on the toggle service map enabled click save connect via satellite save the datadog https //app datadoghq com/organization settings/api keys and https //app datadoghq com/organization settings/application keys in a kubernetes secret, then update the resolve satellite to use these secrets 1 create kubernetes secret create a kubernetes secret using this structure secret creation apiversion v1 kind secret type opaque metadata name datadog resolve token stringdata apikey "\<your api key>" appkey "\<your app key>" security best practice never store credentials in plaintext in configuration files or source control always use kubernetes secrets and encrypt etcd or use enterprise secret management systems see docid 7sxifhtmn0njwjh9w2bfn for detailed guidance apply the secret apply secret kubectl apply f datadog resolve token yml 2 update values file add the datadog integration to resolve values yaml resolve values yaml integrations datadogonprem type "datadog" create true secretname "datadog resolve token" connection site datadoghq com servicemapenabled true (optional) add https //docs resolve ai/sensitive data redaction with redactionconfig resolve values yaml integrations datadogonprem type "datadog" create true secretname "datadog resolve token" connection site datadoghq com servicemapenabled true redactionconfig enabled true 3 apply changes update the satellite with the new resolve values yaml file helm upgrade install resolve satellite \\ oci //registry 1 docker io/resolveaihq/satellite chart \\ \ values resolve values yaml 4 verify integration open https //app0 resolve ai/integrations/datadog/connect and check the connection alert webhooks open https //app0 resolve ai/integrations/datadog/connect and click the integration in the table scroll down and click edit in webhooks click the + button to create a new token name the token monitors and click save keep the configure webhook integration page open in a tab in a secon tab, open the https //app datadoghq com/integrations/webhooks next to webhooks click + new copy fields from the configure webhook integration tab (step 5), click save open your https //app datadoghq com/monitors/manage for each monitor, click edit in conifgure notifications & automations add @webhook resolveai managing monitors with terraform ? add @webhook resolveai to each monitor's message field instead