DataDog · dd-mergequeue · May 19, 2025 · Apr 30, 2025 · Apr 30, 2025 · Apr 30, 2025
@@ -64,19 +64,23 @@ When configuring the Datadog Agent, specify one check instance for each applicat
 ```yaml
 init_config:
 instances:
-  - host: '<SERVER_NAME>.database.windows.net,1433'
+  - host: '<SERVER_NAME>.database.windows.net,<PORT>'
     database: '<DATABASE_1>'
     username: datadog
     password: '<PASSWORD>'
+    connector: 'odbc'
+    driver: 'ODBC Driver 18 for SQL Server'
     # After adding your project and instance, configure the Datadog Azure integration to pull additional cloud data such as CPU, Memory, etc.
     azure:
       deployment_type: 'sql_database'
       fully_qualified_domain_name: '<SERVER_NAME>.database.windows.net'
 
-  - host: '<SERVER_NAME>.database.windows.net,1433'
+  - host: '<SERVER_NAME>.database.windows.net,<PORT>'
     database: '<DATABASE_2>'
     username: datadog
     password: '<PASSWORD>'
+    connector: 'odbc'
+    driver: 'ODBC Driver 18 for SQL Server'
     # After adding your project and instance, configure the Datadog Azure integration to pull additional cloud data such as CPU, Memory, etc.
     azure:
       deployment_type: 'sql_database'
@@ -142,7 +146,7 @@ Create the SQL Server Agent conf file `C:\ProgramData\Datadog\conf.d\sqlserver.d
 init_config:
 instances:
   - dbm: true
-    host: '<HOSTNAME>,<SQL_PORT>'
+    host: '<HOSTNAME>,<PORT>'
     username: datadog
     password: 'ENC[datadog_user_database_password]'
     connector: adodbapi
@@ -180,7 +184,7 @@ The recommended ODBC driver is [Microsoft ODBC Driver][8]. Starting with Agent 7
 
 ```yaml
 connector: odbc
-driver: '{ODBC Driver 18 for SQL Server}'
+driver: 'ODBC Driver 18 for SQL Server'
 ```
 
 Once all Agent configuration is complete, [restart the Datadog Agent][9].
@@ -215,7 +219,7 @@ Create the SQL Server Agent conf file `/etc/datadog-agent/conf.d/sqlserver.d/con
 init_config:
 instances:
   - dbm: true
-    host: '<HOSTNAME>,<SQL_PORT>'
+    host: '<HOSTNAME>,<PORT>'
     username: datadog
     password: 'ENC[datadog_user_database_password]'
     connector: odbc
@@ -258,15 +262,15 @@ Replace the values to match your account and environment. See the [sample conf f
 
 ```bash
 export DD_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
-export DD_AGENT_VERSION=7.51.0
+export DD_AGENT_VERSION=<AGENT_VERSION>
 
 docker run -e "DD_API_KEY=${DD_API_KEY}" \
   -v /var/run/docker.sock:/var/run/docker.sock:ro \
   -l com.datadoghq.ad.check_names='["sqlserver"]' \
   -l com.datadoghq.ad.init_configs='[{}]' \
   -l com.datadoghq.ad.instances='[{
     "dbm": true,
-    "host": "<HOSTNAME>,<SQL_PORT>",
+    "host": "<HOSTNAME>,<PORT>",
     "connector": "odbc",
     "driver": "ODBC Driver 18 for SQL Server",
     "username": "datadog",
@@ -300,9 +304,66 @@ Use the `service` and `env` tags to link your database telemetry to other teleme
 [6]: https://app.datadoghq.com/databases
 {{% /tab %}}
 {{% tab "Kubernetes" %}}
-If you have a Kubernetes cluster, use the [Datadog Cluster Agent][1] for Database Monitoring.
+If you're running a Kubernetes cluster, use the [Datadog Cluster Agent][1] to enable Database Monitoring. If cluster checks aren’t already enabled, [follow these instructions][2] to enable them before proceeding.
 
-If cluster checks are not already enabled in your Kubernetes cluster, follow the instructions to [enable cluster checks][2]. You can configure the Cluster Agent either with static files mounted in the Cluster Agent container, or by using Kubernetes service annotations:
+### Operator
+
+Follow the steps below to set up the SQL Server integration, using the [Operator instructions in Kubernetes and Integrations][6] as a reference.
+
+1. Create or update the `datadog-agent.yaml` file with the following configuration:
+
+    ```yaml
+    apiVersion: datadoghq.com/v2alpha1
+    kind: DatadogAgent
+    metadata:
+      name: datadog
+    spec:
+      global:
+        clusterName: <CLUSTER_NAME>
+        site: <DD_SITE>
+        credentials:
+          apiSecret:
+            secretName: datadog-agent-secret
+            keyName: api-key
+
+      features:
+        clusterChecks:
+          enabled: true
+
+      override:
+        nodeAgent:
+          image:
+            name: agent
+            tag: <AGENT_VERSION>
+
+        clusterAgent:
+          extraConfd:
+            configDataMap:
+              sqlserver.yaml: |-
+                cluster_check: true # Make sure to include this flag
+                init_config:
+                instances:
+                - host: <HOSTNAME>,<PORT>
+                  username: datadog
+                  password: 'ENC[datadog_user_database_password]'
+                  connector: 'odbc'
+                  driver: 'ODBC Driver 18 for SQL Server'
+                  dbm: true
+                  # Optional: For additional tags
+                  tags:  
+                    - 'service:<CUSTOM_SERVICE>'
+                    - 'env:<CUSTOM_ENV>'
+                  # After adding your project and instance, configure the Datadog Azure integration to pull additional cloud data such as CPU, Memory, etc.
+                  azure:
+                    deployment_type: '<DEPLOYMENT_TYPE>'
+                    fully_qualified_domain_name: '<AZURE_ENDPOINT_ADDRESS>'
+    ```
+
+2. Apply the changes to the Datadog Operator using the following command:
+
+    ```shell
+    kubectl apply -f datadog-agent.yaml
+    ```
 
 ### Helm
 
@@ -314,19 +375,20 @@ Complete the following steps to install the [Datadog Cluster Agent][1] on your K
     clusterAgent:
       confd:
         sqlserver.yaml: |-
-          cluster_check: true
+          cluster_check: true # Required for cluster checks
           init_config:
           instances:
           - dbm: true
-            host: <HOSTNAME>,1433
+            host: <HOSTNAME>,<PORT>
             username: datadog
             password: 'ENC[datadog_user_database_password]'
             connector: 'odbc'
-            driver: '{ODBC Driver 18 for SQL Server}'
-            include_ao_metrics: true  # Optional: For AlwaysOn users
-            tags:  # Optional
+            driver: 'ODBC Driver 18 for SQL Server'
+            # Optional: For additional tags
+            tags: 
               - 'service:<CUSTOM_SERVICE>'
               - 'env:<CUSTOM_ENV>'
+            # After adding your project and instance, configure the Datadog Azure integration to pull additional cloud data such as CPU, Memory, etc.
             azure:
               deployment_type: '<DEPLOYMENT_TYPE>'
               fully_qualified_domain_name: '<AZURE_ENDPOINT_ADDRESS>'
@@ -353,12 +415,13 @@ cluster_check: true  # Make sure to include this flag
 init_config:
 instances:
   - dbm: true
-    host: '<HOSTNAME>,<SQL_PORT>'
+    host: <HOSTNAME>,<PORT>
     username: datadog
     password: 'ENC[datadog_user_database_password]'
-    connector: "odbc"
-    driver: '{ODBC Driver 18 for SQL Server}'
-    tags:  # Optional
+    connector: 'odbc'
+    driver: 'ODBC Driver 18 for SQL Server'
+    # Optional: For additional tags  
+    tags: 
       - 'service:<CUSTOM_SERVICE>'
       - 'env:<CUSTOM_ENV>'
     # After adding your project and instance, configure the Datadog Azure integration to pull additional cloud data such as CPU, Memory, etc.
@@ -371,7 +434,6 @@ instances:
 
 Rather than mounting a file, you can declare the instance configuration as a Kubernetes Service. To configure this check for an Agent running on Kubernetes, create a Service in the same namespace as the Datadog Cluster Agent:
 
-
 ```yaml
 apiVersion: v1
 kind: Service
@@ -384,12 +446,12 @@ metadata:
       [
         {
           "dbm": true,
-          "host": "<HOSTNAME>,<SQL_PORT>",
+          "host": "<HOSTNAME>,<PORT>",
           "username": "datadog",
           "password": "ENC[datadog_user_database_password]",
           "connector": "odbc",
           "driver": "ODBC Driver 18 for SQL Server",
-          "tags": ["service:<CUSTOM_SERVICE>", "env:<CUSTOM_ENV>"],  # Optional
+          "tags": ["service:<CUSTOM_SERVICE>", "env:<CUSTOM_ENV>"],
           "azure": {
             "deployment_type": "<DEPLOYMENT_TYPE>",
             "fully_qualified_domain_name": "<AZURE_ENDPOINT_ADDRESS>"
@@ -416,6 +478,7 @@ To avoid exposing the `datadog` user's password in plain text, use the Agent's [
 [3]: /containers/kubernetes/installation/?tab=helm#installation
 [4]: https://github.com/DataDog/integrations-core/blob/master/sqlserver/assets/configuration/spec.yaml#L353-L383
 [5]: /agent/configuration/secrets-management
+[6]: /containers/kubernetes/integrations/?tab=datadogoperator
 {{% /tab %}}
 {{< /tabs >}}
 

@@ -61,7 +61,7 @@ Create the SQL Server Agent conf file `C:\ProgramData\Datadog\conf.d\sqlserver.d
 init_config:
 instances:
   - dbm: true
-    host: '<HOSTNAME>,<SQL_PORT>'
+    host: '<HOSTNAME>,<PORT>'
     username: datadog
     password: '<PASSWORD>'
     connector: adodbapi
@@ -102,7 +102,7 @@ The recommended ODBC driver is [Microsoft ODBC Driver][8]. Starting with Agent 7
 
 ```yaml
 connector: odbc
-driver: '{ODBC Driver 18 for SQL Server}'
+driver: 'ODBC Driver 18 for SQL Server'
 ```
 
 Once all Agent configuration is complete, [restart the Datadog Agent][9].
@@ -137,7 +137,7 @@ Create the SQL Server Agent conf file `/etc/datadog-agent/conf.d/sqlserver.d/con
 init_config:
 instances:
   - dbm: true
-    host: '<HOSTNAME>,<SQL_PORT>'
+    host: '<HOSTNAME>,<PORT>'
     username: datadog
     password: 'ENC[datadog_user_database_password]'
     connector: odbc
@@ -180,7 +180,7 @@ Replace the values to match your account and environment. See the [sample conf f
 
 ```bash
 export DD_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
-export DD_AGENT_VERSION=7.51.0
+export DD_AGENT_VERSION=<AGENT_VERSION>
 
 docker run -e "DD_API_KEY=${DD_API_KEY}" \
   -v /var/run/docker.sock:/var/run/docker.sock:ro \
@@ -222,9 +222,66 @@ Use the `service` and `env` tags to link your database telemetry to other teleme
 [6]: https://app.datadoghq.com/databases
 {{% /tab %}}
 {{% tab "Kubernetes" %}}
-If you have a Kubernetes cluster, use the [Datadog Cluster Agent][1] for Database Monitoring.
+If you're using a Kubernetes cluster, use the [Datadog Cluster Agent][1] for Database Monitoring. If cluster checks aren’t already enabled, [follow these instructions][2] to enable them before proceeding..
 
-If cluster checks are not already enabled in your Kubernetes cluster, follow the instructions to [enable cluster checks][2]. You can configure the Cluster Agent either with static files mounted in the Cluster Agent container, or by using Kubernetes service annotations:
+### Operator
+
+Follow the steps below to set up the SQL Server integration, using the [Operator instructions in Kubernetes and Integrations][6] as a reference.
+
+1. Create or update the `datadog-agent.yaml` file with the following configuration:
+
+    ```yaml
+    apiVersion: datadoghq.com/v2alpha1
+    kind: DatadogAgent
+    metadata:
+      name: datadog
+    spec:
+      global:
+        clusterName: <CLUSTER_NAME>
+        site: <DD_SITE>
+        credentials:
+          apiSecret:
+            secretName: datadog-agent-secret
+            keyName: api-key
+
+      features:
+        clusterChecks:
+          enabled: true
+
+      override:
+        nodeAgent:
+          image:
+            name: agent
+            tag: <AGENT_VERSION>
+
+        clusterAgent:
+          extraConfd:
+            configDataMap:
+              sqlserver.yaml: |-
+                cluster_check: true # Required for cluster checks
+                init_config:
+                instances:
+                - host: <HOSTNAME>,<PORT>
+                  username: datadog
+                  password: 'ENC[datadog_user_database_password]'
+                  connector: 'odbc'
+                  driver: 'ODBC Driver 18 for SQL Server'
+                  dbm: true
+                  # Optional: For additional tags
+                  tags:  
+                    - 'service:<CUSTOM_SERVICE>'
+                    - 'env:<CUSTOM_ENV>'
+                  # After adding your project and instance, configure the Datadog Google Cloud (GCP) integration to pull additional cloud data such as CPU, Memory, etc.
+                  gcp:
+                    project_id: '<PROJECT_ID>'
+                    instance_id: '<INSTANCE_ID>'
+    ```
+
+2. Apply the changes to the Datadog Operator using the following command:
+
+    ```shell
+    kubectl apply -f datadog-agent.yaml
+    ```
 
 ### Helm
 
@@ -240,15 +297,16 @@ Complete the following steps to install the [Datadog Cluster Agent][1] on your K
           init_config:
           instances:
           - dbm: true
-            host: <HOSTNAME>
-            port: 1433
+            host: <HOSTNAME>,<PORT>
             username: datadog
             password: 'ENC[datadog_user_database_password]'
             connector: 'odbc'
-            driver: '{ODBC Driver 18 for SQL Server}'
-            tags:  # Optional
+            driver: 'ODBC Driver 18 for SQL Server'
+            # Optional: For additional tags
+            tags:  
               - 'service:<CUSTOM_SERVICE>'
               - 'env:<CUSTOM_ENV>'
+            # After adding your project and instance, configure the Datadog Google Cloud (GCP) integration to pull additional cloud data such as CPU, Memory, etc.
             gcp:
               project_id: '<PROJECT_ID>'
               instance_id: '<INSTANCE_ID>'
@@ -266,10 +324,6 @@ Complete the following steps to install the [Datadog Cluster Agent][1] on your K
 For Windows, append <code>--set targetSystem=windows</code> to the <code>helm install</code> command.
 </div>
 
-[1]: https://app.datadoghq.com/organization-settings/api-keys
-[2]: /getting_started/site
-[3]: /containers/kubernetes/installation/?tab=helm#installation
-
 ### Configure with mounted files
 
 To configure a cluster check with a mounted configuration file, mount the configuration file in the Cluster Agent container on the path: `/conf.d/sqlserver.yaml`:
@@ -279,13 +333,13 @@ cluster_check: true  # Make sure to include this flag
 init_config:
 instances:
   - dbm: true
-    host: '<HOSTNAME>'
-    port: <SQL_PORT>
+    host: <HOSTNAME>,<PORT>
     username: datadog
     password: 'ENC[datadog_user_database_password]'
-    connector: "odbc"
-    driver: '{ODBC Driver 18 for SQL Server}'
-    tags:  # Optional
+    connector: 'odbc'
+    driver: 'ODBC Driver 18 for SQL Server'
+    # Optional: For additional tags  
+    tags: 
       - 'service:<CUSTOM_SERVICE>'
       - 'env:<CUSTOM_ENV>'
     # After adding your project and instance, configure the Datadog Google Cloud (GCP) integration to pull additional cloud data such as CPU, Memory, etc.
@@ -311,13 +365,12 @@ metadata:
       [
         {
           "dbm": true,
-          "host": "<HOSTNAME>",
-          "port": &l
9331
t;SQL_PORT>,
+          "host": "<HOSTNAME>,<PORT>",
           "username": "datadog",
           "password": "ENC[datadog_user_database_password]",
           "connector": "odbc",
           "driver": "ODBC Driver 18 for SQL Server",
-          "tags": ["service:<CUSTOM_SERVICE>", "env:<CUSTOM_ENV>"],  # Optional
+          "tags": ["service:<CUSTOM_SERVICE>", "env:<CUSTOM_ENV>"],
           "gcp": {
             "project_id": "<PROJECT_ID>",
             "instance_id": "<INSTANCE_ID>"
@@ -340,9 +393,10 @@ To avoid exposing the `datadog` user's password in plain text, use the Agent's [
 
 [1]: /agent/cluster_agent
 [2]: /agent/cluster_agent/clusterchecks/
-[3]: https://helm.sh
+[3]: /containers/kubernetes/installation/?tab=helm#installation
 [4]: https://github.com/DataDog/integrations-core/blob/master/sqlserver/assets/configuration/spec.yaml#L324-L351
 [5]: /agent/configuration/secrets-management
+[6]: /containers/kubernetes/integrations/?tab=datadogoperator
 {{% /tab %}}
 {{< /tabs >}}