Category: Dataverse

Python + Dataverse Series – #07: Running a Linear Normalization Algorithm on Dataverse Data Using Python

This is continuation in this series of Dataverse SDK for Python, if you haven’t checked out earlier articles, I would encourage to start from the beginning of this series.

Python + Dataverse Series – How to run Python Code in Vs Code

Machine learning often begins with one essential step: data preprocessing. Before models can learn patterns, the raw data must be cleaned, scaled, and transformed into a form suitable for analysis. In this example, let me demonstrate how to retrieve numerical data from Microsoft Dataverse and apply a linear normalization algorithm using Python.

Normalization is a fundamental algorithm in machine learning pipelines. It rescales numeric values into a consistent range—typically between 0 and 1—making them easier for algorithms to interpret and compare.

1. Retrieving Data from Dataverse

Using the DataverseClient and Interactive Browser authentication, we connect to Dataverse and fetch the revenue field from the Account table. This gives us a small dataset to run our algorithm on.

from azure.identity import InteractiveBrowserCredential 
from PowerPlatform.Dataverse.client import DataverseClient  
credential = InteractiveBrowserCredential()
client = DataverseClient("https://ecellorsdev.crm8.dynamics.com", credential)
account_batches = client.get(
    "account",
    select=["accountid", "revenue"],
    top=10,
)

We then extract the revenue values into a NumPy array.

2. Implementing the Linear Normalization Algorithm

The algorithm used here is min–max normalization, defined mathematically as:normalized=x−min⁡(x)max⁡(x)−min⁡(x)This algorithm ensures:

  • the smallest value becomes 0
  • the largest becomes 1
  • all other values fall proportionally in between

Here’s the implementation:

import numpy as np
revenues = np.array(revenues)
min_rev = np.min(revenues)
max_rev = np.max(revenues)
normalized_revenues = (revenues - min_rev) / (max_rev - min_rev)

This is a classic preprocessing algorithm used in machine learning pipelines before feeding data into models such as regression, clustering, or neural networks.

3. Visualizing the Normalized Output

To better understand the effect of the algorithm, we plot the normalized values:

import matplotlib.pyplot as plt 
plt.plot(normalized_revenues, marker='o')
plt.title('Normalized Revenues from Dataverse Accounts')
plt.xlabel('Account Index')
plt.ylabel('Normalized Revenue')
plt.grid()
plt.show()

The visualization highlights how the algorithm compresses the original revenue values into a uniform scale.

4. Why Normalization Matters

Normalization is not just a mathematical trick—it’s a crucial algorithmic step that:

  • prevents large values from dominating smaller ones
  • improves convergence in optimization-based models
  • enhances the stability of distance‑based algorithms
  • makes datasets comparable across different ranges
#Running Machine Learning Algorithm on data retrieved from Dataverse to run a linear normalization
from azure.identity import InteractiveBrowserCredential
from PowerPlatform.Dataverse.client import DataverseClient
import numpy as np
# Connect to Dataverse
credential = InteractiveBrowserCredential()
client = DataverseClient("https://ecellorsdev.crm8.dynamics.com", credential)
# Fetch account data as paged batches
account_batches = client.get(
"account",
select=["accountid", "revenue"],
top=10,
)
revenues = []
for batch in account_batches:
for account in batch:
if "revenue" in account and account["revenue"] is not None:
revenues.append(account["revenue"])
revenues = np.array(revenues)
# Apply a simple linear algorithm: Normalize the revenues
if len(revenues) > 0:
min_rev = np.min(revenues)
max_rev = np.max(revenues)
normalized_revenues = (revenues – min_rev) / (max_rev – min_rev)
print("Normalized Revenues:", normalized_revenues)
#visualize the result
import matplotlib.pyplot as plt
plt.plot(normalized_revenues, marker='o')
plt.title('Normalized Revenues from Dataverse Accounts')
plt.xlabel('Account Index')
plt.ylabel('Normalized Revenue')
plt.grid()
plt.show()
view raw Python-RetrieveData-ApplyLinearAlgorithm hosted with ❤ by GitHub

The use of this code is to transform raw Dataverse revenue data into normalized, machine‑learning‑ready values that can be analyzed, compared, and visualized effectively.

You can download the Python Notebook below if you want to work with VS Code

https://github.com/pavanmanideep/DataverseSDK_PythonSamples/blob/main/Python-RetrieveData-ApplyLinearAlgorithm.ipynb

Once you have opened the Python notebook, you can start to run the code as below

You should see something like below

For authentication in another browser tab, once authenticated, you should be able to see the

Hope you found this useful…it’s going to be interesting, stay tuned for upcoming articles.

Cheers,

PMDY

Python + Dataverse Series – #06: Data preprocessing steps before running Machine Learning Algorithms

Hi Folks,

If you were already a Power Platform Consultant and new to working with Python, then I would encourage to start from the beginning of this series.

Python + Dataverse Series – How to run Python Code in Vs Code

Now in this series, we entered an interesting part where Machine learning algorithms were run to analyze Dataverse Data and in this post we will understand why feature scaling is a critical preprocessing step for many machine learning algorithms because it ensures that all features contribute equally to the model’s outcome, prevents numerical instability, and helps optimization algorithms converge faster to the optimal solution

Primarily before running any Machine Learning Algorithm, we need to do some data preprocessing like scaling the data, in this case we will use a formula which is used to scale using min–max normalization (feature scaling to the [0, 1] range).

#preprocessing step before running machine learning algorithms
from azure.identity import InteractiveBrowserCredential #using Interactive Login
from PowerPlatform.Dataverse.client import DataverseClient #installing Python SDK for Dataverse
import numpy as np #import Numpy Library to perform calculations
# Connect to Dataverse
credential = InteractiveBrowserCredential()
client = DataverseClient("https://ecellorsdev.crm8.dynamics.com", credential) #Creates Dataverse Client
# Fetch account data as paged batches
account_batches = client.get(
"account",
select=["accountid", "revenue"],
top=10,
) #Fetches top 10 accounts with accountid, revenue columns
revenues = []
for batch in account_batches:
for account in batch:
if "revenue" in account and account["revenue"] is not None:
revenues.append(account["revenue"])
revenues = np.array(revenues)
#Normalize the revenue
if len(revenues) > 0:
min_rev = np.min(revenues)
max_rev = np.max(revenues)
normalized_revenues = (revenues – min_rev) / (max_rev – min_rev)
print("Normalized Revenues:", normalized_revenues)
#visualize the result
import matplotlib.pyplot as plt
plt.plot(normalized_revenues, marker='o')
plt.title('Normalized Revenues from Dataverse Accounts')
plt.xlabel('Account Index')
plt.ylabel('Normalized Revenue')
plt.grid()
plt.show()
view raw preprocessing step before running machine learning algorithms hosted with ❤ by GitHub

You can download the Python Notebook below if you want to work with VS Code

https://github.com/pavanmanideep/DataverseSDK_PythonSamples/blob/main/Python-PreProcessingStepBeforeMachineLearning.ipynb

Hope you found this useful…

Cheers,

PMDY

Python + Dataverse Series – How to run Python Code in Vs Code

Hi Folks,

As you folks know that Python currently is the number #1 programming language with a massive, versatile ecosystem of libraries for data science, AI, and backend web development. This post kicks off a hands‑on series about working with Microsoft Dataverse using Python. We’ll explore how to use the Dataverse SDK for Python to connect with Dataverse, automate data operations, and integrate Python solutions across the broader Power Platform ecosystem. Whether you’re building data-driven apps, automating workflows, or extending Power Platform capabilities with custom logic, this series will help you get started with practical, real‑world examples.

https://www.microsoft.com/en-us/power-platform/blog/2025/12/03/dataverse-sdk-python/

With the release of the Dataverse SDK for Python, building Python-based logic for the Power Platform has become dramatically simpler. In this post, we’ll walk through how to download Python and set it up in Visual Studio Code so you can start building applications that interact with Dataverse using Python. Sounds exciting already. Let’s dive in and get everything set up..

1. Download and install Python from official website below and then install it in your computer.

https://www.python.org/ftp/python/3.14.3/python-3.14.3-amd64.exe

2. Install VS Code

Important: During installation, make sure to check “Add Python to PATH”. This ensures VS Code can detect Python automatically.

3. After installation, open VS Code and install the Python extension (Microsoft’s official one). This extension enables IntelliSense, debugging, and running Python script

4. That’s it, you are now able to run Python logic inside Vs Code

5. Create or Open a Python file in the system, opened a sample file below

5. If you want to run Python Programmes in your VS Code, follow below options

a. Select Start Debugging

b. You will be prompted a window like below

You can select the first option highlighted above, it automatically runs your Python Code

This is very easy to setup…

If you want to continue reading this series, check out the next article.

Python + Dataverse Series – #01 – Console Application using Python

Hope this helps…

Cheers,

PMDY

Python + Dataverse Series – #05: Remove PII

Hi Folks,

This is in continuation in the Python + Dataverse series, it is worth checking out from the start of this series here.

At times, there will be a need to remove PII(Personally Identifiable Information) present in the Dataverse Environments, for this one time task, you can easily run Python script below, let’s take example of removing PII from Contact fields in the below example.

from azure.identity import InteractiveBrowserCredential
from PowerPlatform.Dataverse.client import DataverseClient
# Connect to Dataverse
credential = InteractiveBrowserCredential()
client = DataverseClient("https://ecellorsdev.crm8.dynamics.com", credential)
#use AI to remove PII data from the dataverse records, let's say contact records
def remove_pii_from_contact(contact):
pii_fields = ['emailaddress1', 'telephone1', 'mobilephone', 'address1_line1', 'address1_city', 'address1_postalcode']
for field in pii_fields:
if field in contact:
contact[field] = '[REDACTED]'
return contact
# Fetch contacts with PII (Dataverse client returns paged batches)
contact_batches = client.get(
"contact",
select=[
"contactid",
"fullname",
"emailaddress1",
"telephone1",
"mobilephone",
"address1_line1",
"address1_city",
"address1_postalcode",
],
top=10,
)
# Remove PII and update contacts
for batch in contact_batches:
for contact in batch:
contact_id = contact.get("contactid")
sanitized_contact = remove_pii_from_contact(contact)
# Prepare update data (exclude contactid)
update_data = {key: value for key, value in sanitized_contact.items() if key != "contactid"}
# Update the contact in Dataverse
client.update("contact", contact_id, update_data)
print(f"Contact {contact_id} updated with sanitized data: {sanitized_contact}")
view raw Easy way to remove PII in Dataverse using Python hosted with ❤ by GitHub

If you want to work on this, download the Python Notebook to use in VS Code…

https://github.com/pavanmanideep/DataverseSDK_PythonSamples/blob/main/Python-DataverseSDK-RemovePII.ipynb

Cheers,

PMDY

Python + Dataverse Series – Post #03: Create, Update, Delete records via Web API

Hi Folks,

This is continuation in this Python with Dataverse Series, in this blog post, we will perform a full CRUD(Create, Retrieve, Update, Delete) in Dataverse using Web API.

Please use the below code for the same…to make any calls using WEB API to Dataverse.

import pyodbc
import msal
import requests
import json
import re
# Azure AD details
client_id = 'XXXX'
client_secret = 'XXXX'
tenant_id = 'XXXX'
authority = f'https://login.microsoftonline.com/{tenant_id}'
resource = 'https://XXXX.crm8.dynamics.com'
# SQL endpoint
sql_server = 'XXXX.crm8.dynamics.com'
database = 'XXXX'
# Get token with error handling
try:
print(f"Attempting to authenticate with tenant: {tenant_id}")
print(f"Authority URL: {authority}")
app = msal.ConfidentialClientApplication(client_id, authority=authority, client_credential=client_secret)
print("Acquiring token…")
token_response = app.acquire_token_for_client(scopes=[f'{resource}/.default'])
if 'error' in token_response:
print(f"Token acquisition failed: {token_response['error']}")
print(f"Error description: {token_response.get('error_description', 'No description available')}")
else:
access_token = token_response['access_token']
print("Token acquired successfully and your token is!"+access_token)
print(f"Token length: {len(access_token)} characters")
except ValueError as e:
print(f"Configuration Error: {e}")
print("\nPossible solutions:")
print("1. Verify your tenant ID is correct")
print("2. Check if the tenant exists and is active")
print("3. Ensure you're using the right Azure cloud (commercial, government, etc.)")
except Exception as e:
print(f"Unexpected error: {e}")
#Get 5 contacts from Dataverse using Web API
import requests
import json
try:
#Full CRUD Operations – Create, Read, Update, Delete a contact in Dataverse
print("Making Web API request to perform CRUD operations on contacts…")
# Dataverse Web API endpoint for contacts
web_api_url = f"{resource}/api/data/v9.2/contacts"
# Base headers with authorization token
headers = {
'Authorization': f'Bearer {access_token}',
'OData-MaxVersion': '4.0',
'OData-Version': '4.0',
'Accept': 'application/json',
'Content-Type': 'application/json'
}
# Create a new contact
new_contact = { "firstname": "John", "lastname": "Doe" }
print("Creating a new contact…")
# Request the server to return the created representation. If not supported or omitted,
# Dataverse often returns 204 No Content and provides the entity id in a response header.
create_headers = headers.copy()
create_headers['Prefer'] = 'return=representation'
response = requests.post(web_api_url, headers=create_headers, json=new_contact)
created_contact = {}
contact_id = None
# If the API returned the representation, parse the JSON
if response.status_code in (200, 201):
try:
created_contact = response.json()
except ValueError:
created_contact = {}
contact_id = created_contact.get('contactid') or created_contact.get('contactid@odata.bind')
print("New contact created successfully (body returned).")
print(f"Created Contact ID: {contact_id}")
# If the API returned 204 No Content, Dataverse includes the entity URL in 'OData-EntityId' or 'Location'
elif response.status_code == 204:
entity_url = response.headers.get('OData-EntityId') or response.headers.get('Location')
if entity_url:
# Extract GUID using regex (GUID format)
m = re.search(r"([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12})", entity_url)
if m:
contact_id = m.group(1)
created_contact = {'contactid': contact_id}
print("New contact created successfully (no body). Extracted Contact ID from headers:")
print(f"Created Contact ID: {contact_id}")
else:
print("Created but couldn't parse entity id from response headers:")
print(f"Headers: {response.headers}")
else:
print("Created but no entity location header found. Headers:")
print(response.headers)
else:
print(f"Failed to create contact. Status code: {response.status_code}")
print(f"Error details: {response.text}")
# Read the created contact
if not contact_id:
# Defensive: stop further CRUD if we don't have an id
print("No contact id available; aborting read/update/delete steps.")
else:
print("Reading the created contact…")
response = requests.get(f"{web_api_url}({contact_id})", headers=headers)
if response.status_code == 200:
print("Contact retrieved successfully!")
contact_data = response.json()
print(json.dumps(contact_data, indent=4))
else:
print(f"Failed to retrieve contact. Status code: {response.status_code}")
print(f"Error details: {response.text}")
# Update the contact's email
updated_data = { "emailaddress1": "john.doe@example.com" }
response = requests.patch(f"{web_api_url}({contact_id})", headers=headers, json=updated_data)
if response.status_code == 204:
print("Contact updated successfully!")
else:
print(f"Failed to update contact. Status code: {response.status_code}")
print(f"Error details: {response.text}")
# Delete the contact
response = requests.delete(f"{web_api_url}({contact_id})", headers=headers)
if response.status_code == 204:
print("Contact deleted successfully!")
else:
print(f"Failed to delete contact. Status code: {response.status_code}")
print(f"Error details: {response.text}")
except requests.exceptions.RequestException as e:
print(f"Request error: {e}")
except KeyError as e:
print(f"Token not available: {e}")
except Exception as e:
print(f"Unexpected error: {e}")
view raw FullCRUDWebAPI hosted with ❤ by GitHub

You can use the VS Code as IDE, copy the above code in a python file, next click on Run Python File at the top of the VS Code

Hope this helps someone making Web API Calls using Python.

If you want to try this out, download the Python Notebook and open in VS Code.

https://github.com/pavanmanideep/DataverseSDK_PythonSamples/blob/main/Python-Dataverse-FullCRUD-03.ipynb

Looking to continue following this series, don’t forget the next article in this series

Python + Dataverse Series – #04: Create records in batch using Execute Multiple

Cheers,

PMDY

Adding intelligence to Dataverse using Dataverse AI functions

Hi Folks,

While intelligence with the use of AI is being embedded into each and every part of the Microsoft ecosystem, it is good to know the features coming in the Power Platform space.

In this blog post, let’s see how we can use Dataverse AI Functions, their usage, advantages which can greatly ease summarizing, classifying, extracting, translating, assessing sentiment, or drafting a reply for common business scenarios.

To illustrate it better, I used a different AI Function for Canvas App, Model Driven App and Power Automate, hope you can follow the same for others as well.

What are Dataverse AI Functions?

Think of Dataverse AI Functions as prebuilt AI Functions which will add intelligence in your Apps and Flows without need to collect, build and train the models. They can be used in many places such as AI Builder, Power Automate, Power Apps, Low Code Plugins. Following are the AI Functions available…

  1. AIReply – Drafts a reply to the message you provide.
  2. AISentiment – Detects sentiment for the text you provide
  3. AISummarize – Summarizes the text you provide
  4. AIClassify – Classifies the text into one or more categories, you can use this from custom copilot
  5. AIExtract – Extracts specified entities like Names of people, phone numbers, places etc.
  6. AITranslate – Translate text from another language

Let’s see their usage in our favorite Canvas Apps first as illustration is easy and later in the post, I will mention how you can call the Dataverse AI Functions from Model Driven Apps, Power Automate so that you can get the real essence of it….

Utilizing ‘Dataverse AI Functions’ in a Canvas App

Create a new Canvas App and add ‘Environment’ Datasource as shown below.

All the ‘Dataverse AI functions’ can be accessed by ‘Environment‘ as shown below.

Let’s try the AIReply function in Canvas Apps

Add a textbox for storing the prompt or input string and a button control.

On the ‘OnSelect‘ event of the button, use the following formula to store the response in the AIResponse context variable, make sure you name the variables appropriately in your formula as per your naming defined in canvas apps.

UpdateContext({AIReplyResponse:Environment.AIReply({Text:AIInput.Text})})
view raw Dataverse AI Function hosted with ❤ by GitHub

Now create one more text variable to store the response and change the Default value to AIReplyResponse.PreparedResponse.

Try testing the app by providing inputs as below…

You should get a response from AIReply in the response field, you can try out other functions providing the necessary parameters required.

Utilizing ‘Dataverse AI Functions’ in a Power Automate

In Power Automate, all you can do to call Dataverse AI Functions is call the Unbound Action as below.

Passing the relevant input parameters is enough to get the output from these functions.

Let’s try AISentiment

Click on test, you should a response from Power Automate with the sentiment

Utilizing ‘Dataverse AI Functions’ in a Model Driven Apps

Do you want to utilize the similar capabilities of Dataverse AI Functions inside your custom code like in Plugins, Actions etc..

Let’s try AIClassify

var request = new OrganizationRequest("AIClassify")
{
["AllowMultipleCategories"] = false,
["Categories"] = titles,
["Text"] = classifyText
};
var result = service.Execute(request);
view raw DataverseFunctions.cs hosted with ❤ by GitHub

It was pretty much similar in AIBuilder as well…

Please do note that there are quotas to use these AI Functions at tenant level, else you might get similar error like below, while I didn’t get any information regarding this from Microsoft, so I am unsure about this as of writing this post, I will keep this updated if I get to know.

Using Dataverse AI functions needs a bit of Prompt Engineering knowledge, you were looking to learn more about Prompt engineering, then check it out here.

References:

https://learn.microsoft.com/en-us/power-platform/power-fx/reference/function-ai

Cheers,

PMDY

Simplify Power BI Management with Environment Variables

Introduction

Power Platform solutions often rely on dynamic configuration data, like Power BI workspace IDs, report URLs, or API endpoints. Environment variables make it easier to manage such configurations, especially in managed solutions, without hard coding values. This blog will walk you through the steps to update a Power BI environment variable in managed solutions, focusing on the task of switching the workspace to the correct one directly within Power BI integration when working on different environments.

What are Environment Variables in Power Platform?

Before we dive into the steps, let’s quickly cover what environment variables are and their role in solutions:

  • Environment Variables are settings defined at the environment level and can be used across apps, flows, and other resources in Power Platform.
  • They store values like URLs, credentials, or workspace IDs that can be dynamically referenced.
  • In managed solutions, these variables allow for configuration across multiple environments (e.g., development, testing, production).

Why Update Power BI Environment Variables in Managed Solutions?

Updating environment variables for Power BI in managed solutions ensures:

  • Simplified Management: You don’t need to hardcode workspace or report IDs; you can simply update the values as needed.
  • Better Configuration: The values can be adjusted depending on which environment the solution is deployed in, making it easier to scale and maintain.
  • Dynamic Reporting: Ensures that Power BI reports or dashboards are correctly linked to the right workspace and data sources.
  • Best and Recommended: Changing the environment variables and pointing to right workspace is the correct and is best way to point your Power BI Report to respective workspace and recommended by Microsoft.

Prerequisites

Before proceeding with the update, ensure you meet these prerequisites:

  1. You have the necessary permissions to edit environment variables and manage solutions.
  2. The Power BI integration is already set up within your Power Platform environment.
  3. You have a managed solution where the Power BI environment variable is defined.

Steps to Update a Power BI Environment Variable in Managed Solutions

Step 1: Navigate to the Power Platform Admin Center
Step 2: Open the Solution in Which the Environment Variable is Defined
  • Go to Solutions in the left navigation menu.
  • Select the Managed Solution that contains the Power BI environment variable you need to update.
Step 3: Find the Environment Variable
  • In the solution, locate Environment Variables under the Components section.
  • Identify the Power BI environment variable (such as API URL or workspace ID) that you need to modify.
Step 4: Click on Dashboards to Update the Workspace
  • To update the Power BI environment variable related to the workspace, click on Dashboards.
  • Find the existing environment variable tied to the workspace and click to edit it.
  • Here, you’ll see the current workspace configuration for the Power BI resource.
Step 5: Update the Workspace ID
  • In the environment variable settings, you will now change the workspace to the new one.
  • Select the appropriate workspace from the list or manually enter the new workspace ID, ensuring it aligns with the target environment (development, production, etc.).
  • If necessary, update other properties like report or dataset IDs based on your environment needs.
Step 6: Save and Apply Changes
  • After updating the workspace and any other relevant properties, click Save.
  • The environment variable will now reflect the new workspace or configuration.
Step 7: Publish the Solution
  • If you’re using a managed solution, ensure that the updated environment variable is properly published to apply the changes across environments.
  • You may need to export the solution to other environments (like test or production) if applicable.
Step 8: Test the Changes
  • After saving and publishing, test the Power BI integration to ensure that the updated workspace is correctly applied.
  • Check the relevant Power BI reports, dashboards, or flows to confirm that the new workspace is being used.

Best Practices

  • Document Changes: Always document the updates to environment variables, including what changes were made and why.
  • Use Descriptive Names: When defining environment variables, use clear and descriptive names to make it easy to understand their purpose.
  • Cross-Environment Testing: After updating environment variables, test them in different environments (dev, test, prod) to ensure consistency and reliability.
  • Security Considerations: If the environment variable includes sensitive information (like API keys), make sure it’s properly secured.

Conclusion

Updating Power BI environment variables in managed solutions allows you to maintain flexibility while keeping your configurations centralized and dynamic. By following the steps outlined in this blog post, you can efficiently manage workspace IDs and other key configuration data across multiple environments. This approach reduces the need for hardcoded values and simplifies solution deployment in Power Platform.

Cheers,

PMDY

Dataverse – Git Integration – Preview – Quick Review

Hi Folks,

This post is about Dataverse and Git Integration which is the most sought after feature in the todays automation Era. This is a preview feature, you would need to create a new environment with Early Access enabled to test this feature or you can use an existing US Preview environment for testing this out.

While every MDA(Model Driven Application) and it’s components can be safely and moved across the environments using Solutions with the help of Azure DevOps Pipelines. However when coming to integrating Power Platform Solutions to Azure DevOps, we had to manually export the solution and download them each and every time when we would like to commit the Solution Artifacts to Azure DevOps Repo.

With this new Preview feature we can directly integrate the Power Platform Solutions to Azure DevOps.

Let’s see this action…wait a moment, there were some prerequisites to be considered…

  1. Environment should be a Managed Environment to start using this and you need to be an Admin for the environment
  2. Azure DevOps subscription and license should be available to set this up, also permission to read source files and commits(should be a member of contributor group in Azure DevOps) from a Repo
  3. Your email address used for Azure DevOps and Power Platform Solutions should be the same

Setup:

Connecting Dataverse with Azure DevOps is easy but requires a bit of understanding of the Binding options available.

Well, there were two types of Binding options

  1. Environment Binding – Single root folder binds to all the unmanaged solutions in the environment
  2. Solution Binding – Different solutions uses a different root folder in Azure DevOps for binding

Note: Once the binding is setup, there isn’t away to change, so set this up carefully, else you may need to delete the folder and create a new one in Azure DevOps.

Let’s see one by one…for demoing purpose, I have created two projects in Azure DevOps Instance

  1. Solution Binding: When we use this, all the components will be available as pending changes
  2. Environment Binding: When we use this, all the unmanaged solution components will be mapped to one Azure DevOps root folder. Let’s set this up.

We are currently able to use only Solution binding, as Environment Binding doesn’t show up any changes to be committed, but there is a catch here.

We can set up for Environment binding and verify if the Solution components are getting marked as pending changes or not. Do note that Setting up the Binding is a one time activity for environment, once setup, it can’t be changed from one type to another.

Open https://make.powerapps.com and navigate to solutions and click on ellipses as below

Once clicked on Connect to Git

Since we were currently using Environment binding, let’s select the Connection Type as Environment

Then click on Connect, once connected, you should a alert message in power apps maker portal at the top.

Now create a new solution as below named ecellors Solution

Verify the integration by clicking on Git Integration as below

It should show as below

Now let’s add few components to the solution we created

Once added, let’s publish the unmanaged solution and verify it..

Do look closely, you should see a Source Control icon highlighted in yellow color for illustration.

Also, you should see a commit option available at the top

You should now be able to commit the solution components as if you are committing the code changes.

It also specifies the branch to which we were commiting…

While it takes few minutes unlike pushing the code to Azure DevOps to push the changes, however this would depend based on the number of solution components you were pushing..once it is done, it will show a commit message like below…

Now let’s verify our Azure DevOps Repo..for this you can go back to the main solutions page, click on Git Connection at the top..

After clicking on Git Connection, click on the link to Microsoft Azure DevOps as below

Then you should be navigated to Azure DevOps folder as below where all the solution files will be tracked component wise.

Now we will move back to Power Apps maker portal and make some changes to any of the components inside the solution…

Let’s say, I just edited the flow name and created a new connection reference, saved and published the customizations.

If you did some changes at the Azure DevOps repo level, you can come back and click on Check for updates, if there were any conflicts between changes done in Azure DevOps and component in solution, it will be shown as conflict.

We now have 3 component changes and all were listed here…you can click on Commit.

As soon as the changes are committed, you should see a message saying Commit Successful and 0 Changes, 0 Updates, 0 Conflicts.

Now you successfully integrated Dataverse Solution components with Azure DevOps without any manual intervention required while deploying solutions using Azure DevOps Pipelines.

Hope you learned something new today…while feature is still in Preview and only available for early release, while couple of issues still need to fixed by Microsoft.

I have tested this feature by creating an environment in US Preview region and this feature will be a good value to projects using Automation and this solution repository can be further deployed to other environments using Azure DevOps Pipelines.

This will be rolled out soon next year, hope you learned something new today…

Cheers,

PMDY

Creating In-App Notifications in Model Driven Apps in an easier way – Quick Review

Hi Folks,

In App notifications are trending these days where many customers are showing interest in implementing these for their businesses.

So, in this blog post, I am going to show you the easiest way to generate In App notification using XrmToolBox in few clicks. Use the below tool to generate one.

So, let me walk you through step by step

Step 1: Open In App Notification Builder in XrmToolBox

Step 2: In App notification is a setting that should be enabled at App level, so meaning if you have developed few Model Driven Apps, you will be able to enable the In App notification individually for each one of them.

Step 3: In the above snapshot, we should be able to select the respective App for which we want to enable the In App Notification. Red bubble besides indicate that the In App notification is not enabled for this App.

So, we need to enable it by clicking on the red icon itself, you should then be able to get this prompt as below.

Step 5: Upon confirming the confirmation dialog box, the In App notification will be enabled for that App and you the red button turns to green as below saying that In App Notification is enabled.

Now that the In App notification is enabled in the App, we will proceed with the remaining setup.

Step 6: You can proceed to give a meaningful title, body for you In App Notification. Also mention the required toast type and specify the expiry duration, Icon. Also Click on Add icon and choose the action required to be performed when In App notification is clicked.

Step 9: You can even choose the type of action to be performed…

For example, let’s use to open as dialog and show list view

Your screen should look something like below

Step 10: Once done, you can click on create and that’s it you have now created In App Notification. Now let’s test this for the user who have priveleges to access this App.

If not, you will face this error..

Log in with user account for which the In App Notification is triggered.

Hurray!!!! That’s it, how easy it was to create In App Notification in Low Code manner.

You can even get the code behind this as well…

However, there were other ways to trigger the In App Notification from a Pro Code angle, let’s discuss those as well.

In this case you need to manually turn the In App Notification feature on by going to settings for the Model Driven App as below first.

Notifications can be sent using the SendAppNotification message using SDK.

You can either trigger from and can choose based on your convenience to trigger a similar notification.

Client Scripting

    var systemuserid = '<user-guid>';
    var data = {
    "actions": [
    {
    "data": {
    "url": "?pagetype=entitylist&etn=account&viewid=00000000-0000-0000-00aa-000010001002",
    "navigationTarget": "dialog"
    },
    "title": "Link to list of notifications"
    }
    ]
    };
    var notificationRecord =
    {
    'title': 'Learning In App Notificaiton',
    'body': `In-App Notifications in Model-Driven Apps are messages or alerts designed to notify users of important events or actions within the app. These notifications appear directly inside the application, providing a seamless way to deliver information without relying on external methods such as emails.`,
    'ownerid@odata.bind': '/systemusers(' + systemuserid + ')',
    'icontype': 100000003, // Warning
    'toasttype': 200000000, // Timed
    'ttlinseconds': 1209600,
    'data': JSON.stringify(data)
    }
    Xrm.WebApi.createRecord('appnotification', notificationRecord).
    then(
    function success(result) {
    console.log('notification created with single action: ' + result.id);
    },
    function (error) {
    console.log(error.message);
    // handle error conditions
    }
    );
    view raw JS hosted with ❤ by GitHub

      Plugin/SDK

      var notification = new Entity("appnotification")
      {
      ["title"] = @"Learning In App Notificaiton",
      ["body"] = @"In-App Notifications in Model-Driven Apps are messages or alerts designed to notify users of important events or actions within the app. These notifications appear directly inside the application, providing a seamless way to deliver information without relying on external methods such as emails.",
      ["ownerid"] = new EntityReference("systemuser", new Guid("00000000-0000-0000-0000-000000000000")),
      ["icontype"] = new OptionSetValue(100000003), // Warning
      ["toasttype"] = new OptionSetValue(200000000), // Timed
      ["ttlinseconds"] = 1209600,
      ["data"] = @"{
      ""actions"": [
      {
      ""data"": {
      ""url"": ""?pagetype=entitylist&etn=account&viewid=00000000-0000-0000-00aa-000010001002"",
      ""navigationTarget"": ""dialog""
      },
      ""title"": ""Link to list of notifications""
      }
      ]
      }"
      };
      service.Create(notification);
      view raw gistfile1.txt hosted with ❤ by GitHub

      Power Automate:

      You should design your Power Automate something like below to trigger a similar notification.

        Note: Currently In App Notification will be triggered for only Model Driven Apps.

        Reference:

        In App Notification Documentation

        Hope this saves some of your time…

        Cheers,

        PMDY

        Understanding Dataverse search in Dynamics 365 – Quick Review

        Hi Folks,

        One of my colleagues asked about Dataverse search, hence I am writing this article on Dataverse Search in Dynamics 365 and in the end, will compare different search options available in Dynamics 365.

        Dataverse Search:

        In layman terms, Dataverse Search is a powerful search tool that helps you find information quickly across your organization’s data in Microsoft Dataverse, which is the underlying data platform for apps like Power Apps, Dynamics 365, and more, shows you all the related information from across different tables or records in one place.

        In short, Dataverse Search is the evolved version of Relevance Search, offering a more robust, faster, and user-friendly search experience including search results for text in documents that are stored in Dataverse such as PDF, Microsoft Office documents, HTML, XML, ZIP, EML, plain text, and JSON file formats. It also searches text in notes and attachments. Before enabling it, just note that once Dataverse search is enabled, it will be affected in all your Model Driven Apps, as of now, just take note.

        It is on by default, here is where you can now turn off the Dataverse Search:

        1. Navigate to https://admin.powerplatform.com
        2. Click on Environments –> Choose your required environment –> Settings –>Features

        3. Disable/Enable the Dataverse search feature.

        Once enabled, we need to configure the tables for Dataverse Search so that indexing is performed at the backend, in order to do this…

        1. Navigate to https://make.powerapps.com, select your desired solution –> Click on Overview as shown below

        Now you need to choose Manage Search Index and you can choose your desired table and fields, there isn’t a limit on the number of tables you can configure, but there is a limit on the number of fields you can configure for an environment, a maximum of 1000 fields are permitted both including system and custom fields, 50 fields are used by system, so you can configure 950 fields.

        Just note that some field types are treated as multiple fields in the Dataverse search index as indicated in this table.

        Field typeNumber of fields used in
        the Dataverse search index
        Lookup (customer, owner, or Lookup type attribute)3
        Option Set (state, or status type attribute)2
        All other types of fields1

        At the bottom of the snap above, you could see the percentage of columns indexed in this environment.

        When Dataverse search is enabled, the search box is always available at the top of every page in your app. You can start a new search and quickly find the information that you’re looking for.

        When Dataverse search is turned on, it becomes your default and only global search experience for all of your model-driven apps. You won’t be able to switch to quick find search also known as categorized search.

        You can also enable Quick actions as shown in the below table

        TableQuick actions
        AccountAssign, Share, Email a link
        ContactAssign, Share, Email a link
        AppointmentMark complete, Cancel, Set Regarding, Assign, Email a link
        TaskMark complete, Cancel, Set Regarding, Assign, Email a link
        Phone CallMark complete, Cancel, Set Regarding, Assign, Email a link
        EmailCancel, Set Regarding, Email a link

        Here is the short table comparing all types of searches in Dynamics 365…

        FunctionalityDataverse searchQuick FindAdvanced Find
        Enabled by default?Yes.
        Note: For non-production environments an administrator must manually enable it.        		Yes, for the table grid.
        No, for multiple-table quick find (categorized search). An administrator must first disable Dataverse search before multiple-table grid find can be enabled.        		Yes
        Single-table search scopeNot available in a table grid. You can filter the search results by a table on the results page.Available in a table grid.Available in a table grid.
        Multi-table search scopeThere is no maximum limit on the number of tables you can search.Searches up to 10 tables, grouped by a table.Multi-table search not available.
        Search behaviorFinds matches to any word in the search term in any column in the table.Finds matches to all words in the search term in one column in a table; however, the words can be matched in any order in the column.Query builder where you can define search criteria for the selected row type. Can also be used to prepare data for export to Office Excel so that you analyze, summarize,or aggregate data, or create PivotTables to view your data from different perspectives.
        Searchable columnsText columns like Single Line of Text, Multiple Lines of Text, Lookups, and Option Sets. Doesn’t support searching in columns of Numeric or Date data type.All searchable columns.All searchable columns.
        Search resultsReturns the search results in order of their relevance, in a single list.For single-table, returns the search results in a table grid. For multi-table, returns the search results grouped by categories, such as accounts, contacts, or leads.Returns search results of the selected row type with the columns you have specified, in the sort order you have configured.

        Hope you learned something today…if you have any questions, do let me know in the comments…

        Cheers,

        PMDY