Add data flow known issues

Author: Pat Altimore

articles/iot-operations/connect-to-cloud/howto-configure-dataflow-profile.md

@@ -19,7 +19,7 @@ Data flow profiles can be used to group data flows together so that they share t
 
 The most important setting is the instance count. For a given data flow, the instance count determines the number of copies that run on your cluster. For example, you might have a data flow profile with a single instance for development and testing, and another profile with multiple instances for production. Or, you might use a data flow profile with low instance count for low-throughput data flows and a profile with high instance count for high-throughput data flows. Similarly, you can create a data flow profile with different diagnostic settings for debugging purposes.
 
-You should avoid associating too many data flows with a single data flow profile. If you have a large number of data flows, create multiple data flow profiles to reduce the risk of exceeding the data flow profile configuration size limit.
+You should avoid associating too many data flows with a single data flow profile. If you have a large number of data flows, create multiple data flow profiles to reduce the risk of exceeding the data flow profile configuration size limit of 70.
 
 ## Default data flow profile
 

articles/iot-operations/connect-to-cloud/howto-create-dataflow.md

@@ -56,7 +56,7 @@ You can deploy data flows as soon as you have an instance of [Azure IoT Operatio
 
 ### Data flow profile
 
-If you don't need different scaling settings for your data flows, use the [default data flow profile](./howto-configure-dataflow-profile.md#default-data-flow-profile) provided by Azure IoT Operations. You should avoid associating too many data flows with a single data flow profile. If you have a large number of data flows, distribute them across multiple data flow profiles to reduce the risk of exceeding the data flow profile configuration size limit.
+If you don't need different scaling settings for your data flows, use the [default data flow profile](./howto-configure-dataflow-profile.md#default-data-flow-profile) provided by Azure IoT Operations. You should avoid associating too many data flows with a single data flow profile. If you have a large number of data flows, distribute them across multiple data flow profiles to reduce the risk of exceeding the data flow profile configuration size limit of 70.
 
 To learn how to configure a new data flow profile, see [Configure data flow profiles](howto-configure-dataflow-profile.md).
 

articles/iot-operations/troubleshoot/known-issues.md

@@ -4,7 +4,7 @@ description: Known issues for the MQTT broker, Layered Network Management (previ
 author: dominicbetts
 ms.author: dobett
 ms.topic: troubleshooting-known-issue
-ms.date: 05/22/2025
+ms.date: 07/30/2025
 ---
 
 # Known issues: Azure IoT Operations
@@ -302,3 +302,62 @@ Log signature: N/A
 When you connect multiple IoT Operations instances to the same Event Grid MQTT namespace, connection failures might occur due to client ID conflicts. Client IDs are currently derived from data flow resource names, and when using infrastructure as code patterns for deployment, the generated client IDs might be identical.
 
 To work around this issue, add randomness to the data flow names in your deployment templates.
+
+### A data flow profile can't exceed 70 data flows
+
+---
+
+Issue ID: 0313
+
+---
+
+Log signature:
+
+`exec /bin/main: argument list too long`
+
+---
+
+If you create more than 70 data flows for a single data flow profile, deployments fail with the error `exec /bin/main: argument list too long`.
+
+To work around this issue, create multiple data flow profiles and distribute the data flows across them. Don't exceed 70 data flows per profile.
+
+### Complex data might be flattened when enriching data in a data flow
+
+---
+
+Issue ID: 7385
+
+---
+
+Log signature: N/A
+
+---
+
+When enriching data using complex object DSS reference data, the output may be moved to the root level instead of preserving the original structure.
+
+For example, if you have a complex object with properties like:
+
+```json
+{
+  "complex_property_1": {
+      "field1": 12,
+      "field2": 13 
+  },
+  "complex_property_2": {
+     "field2": 24
+  }
+}
+```
+
+The output might look like:
+
+```json
+{
+  "property_1": 2,
+  "property_2": 3,
+  "field1": 12,
+  "field2": 24,
+}
+```
+
+The complex properties are flattened to the root, and the original structure is lost. If fields with the same name exist in the complex objects or the root, the values might overwrite the root values. In the example, `field2` from `complex_property_2` overwrites the `field2` from `complex_property_1`.