Instant insights, automation and action – Part 4 Register Power BI in Azure Active Directory

This is the fourth post in a series of articles in which I explain how to integrate Power BI, Power Apps, Flow, Azure Machine Learning and Dynamics 365 to rapidly build a functioning system which allows users to analyze, insert, automate and action data.

In the previous article I covered building the Power BI Report.

In this article I will cover how to enable data to be pushed into Power BI use Flow. This is a fast no code solution.


This is a one-time setup that is required in order to use the Power BI connector in MS Flow. If you do not do this step you will see an error screen in MS Flow like the screen clip below.


Prerequisites

In order to complete this tutorial, you will need permission to register applications in your Azure Active Directory tenant.


For more information on the Azure AD Tenant you can click the following link.

https://docs.microsoft.com/en-us/power-bi/developer/create-an-azure-active-directory-tenant

Power BI Development Center

Log onto the Power BI Development Center and enable API features and get the key to register the app in Azure.

Go to the following URL and sign in.

https://dev.powerbi.com/apps


Enter in a meaningful name for your app, I called mine AnthonysPowerBIApp but you can call yours whatever you would like. Choose Native for the Application Type and select Read all datasets and Read and write all datasets for the API Access


Click on Register. A screen like the one below should pop up. Be sure to copy down the Application ID as this is needed to register the application in Azure.


Azure Portal

Next log onto the azure portal using the following URL https://portal.azure.com/#home

Once in the portal admin page navigate to the Azure Active Directory menu blade


Next click on App registrations and select the app that we created using the Power BI Development Center.


You can change settings in the app if you whish to tailor it be clicking on Properties.

Now that the Power BI App has been registered in Azure Active Directory you can use it in various Microsoft cloud services such as Flow.


As you can see in the image above, I no longer get a permission error and I am able to select the workspace, dataset and table.


In the next post we will build out the flow so that data is passed from the Power App to an Azure Machine Learning experiment for scoring and then into the Power BI API Enabled Dataset for real-time analytics.

Hopefully you have found this to be another practical post.

Until next time

Anthony

References

Here is the official documentation from Microsoft on how to register Power BI to push data into it using REST API calls.

https://docs.microsoft.com/en-us/power-bi/developer/overview-of-power-bi-rest-api


Instant insights, automation and action – Part 3 Create the Power BI Report

This is the third post in a series of articles in which I explain how to integrate Power BI, Power Apps, Flow, Azure Machine Learning and Dynamics 365 to rapidly build a functioning system which allows users to analyze, insert, automate and action data.

In the previous article I covered building the Power App. In this article I will cover the Power BI report.

We will build out this system in the following order; Power App, Azure Machine Learning, Power BI and then last MS Flow to connect the components. Before you can begin this tutorial there are some required prerequisites.

Prerequisites

  • Power Apps
  • MS Flow
  • Power BI Pro or Premium
  • Access to Azure Active Directory to register Power BI App
  • Dynamics 365

Create the API Enabled Dataset

Log onto Power BI and create a new app workspace called Customer Segmentation. This step is not required however if you are like me you create a lot of different content so it’s a good habitat to get into so that you can better manage your work.


In case you are wondering the screen clip above is using the new App Workspace experience. Next, we will create a new streaming data set.

On the splash page for the app click Skip at the bottom right corner of the page.


Now select +Create > Streaming dataset.


Select API and click next.


Next create the WholeSaleCustomer dataset.

It will have the following field names and data types

Field Name Data Type
Customer Name Text
Channel Number
Region Number
Fresh Number
Milk Number
Grocery Number
Frozen Number
Detergents_Paper Number
Delicassen Number
Category Number


Click the Create button to generate the dataset.

Next, we will leverage the generated PowerShell script to create some test records in our newly formed dataset. Click on PowerShell and copy the code into Notepad.


We will create three test records by running the PowerShell code below. Modify the code you coped into Notepad so that it looks simlar to the code below. Before you can run this you will need to replace <Your Key> with the key displayed in your Power BI service.

$endpoint = "https://api.powerbi.com/beta/8c17d9d4-2652-4573-8a9c-d5dde0750715/datasets/13b74183-5eb2-480b-ba11-c0af0ecbdd26/rows?
key=<Your Key>

$payload = @{
"Customer Name" ="Test1"
"Channel" =1
"Region" =1
"Fresh" =98.6
"Milk" =98.6
"Grocery" =98.6
"Frozen" =98.6
"Detergents_Paper" =98.6
"Delicassen" =98.6
"Category" =0
}


Invoke-RestMethod -Method Post -Uri "$endpoint" -Body (ConvertTo-Json @($payload))
$payload = @{
"Customer Name" ="Test2"
"Channel" =2
"Region" =2
"Fresh" =98.6
"Milk" =98.6
"Grocery" =98.6
"Frozen" =98.6
"Detergents_Paper" =98.6
"Delicassen" =98.6
"Category" =1
}


Invoke-RestMethod -Method Post -Uri "$endpoint" -Body (ConvertTo-Json @($payload))
$payload = @{
"Customer Name" ="Test3"
"Channel" =3
"Region" =3
"Fresh" =98.6
"Milk" =98.6
"Grocery" =98.6
"Frozen" =98.6
"Detergents_Paper" =98.6
"Delicassen" =98.6
"Category" =2
}


Invoke-RestMethod -Method Post -Uri "$endpoint" -Body (ConvertTo-Json @($payload))

To do this launch PowerShell in Administrator mode and copy and paste the code into the PowerShell desktop app.


The data set now has three records in it and you can start to use it in Power BI. To do this go to the dataset and click the three dots beside the name of the dataset. This will open a new report with a blank canvas. Add a table and drop all of the fields from the data set into the visual.


As you may notice from the screen shot above the fields Fresh, Milk, Grocery, Frozen, Detergents_Paper and Delicassen are not formatted as currency but should be. Unfortunately, API enabled data sets only have three data types Text, Number and Date and no formatting options so we cannot specify that these fields are currency fields.

Thankfully we can leverage the Report level measures for live connections to Analysis Services tabular models & Power BI service datasets feature that was released in May 2017 to add new measures with the proper currency data type defined.
Continue reading “Instant insights, automation and action – Part 3 Create the Power BI Report”

Instant insights, automation and action – Part 2 Create Azure Machine Learning Experiment

This is the second post in a series of articles in which I explain how to integrate Power BI, Power Apps, Flow, Azure Machine Learning and Dynamics 365 to rapidly build a functioning system which allows users to analyze, insert, automate and action data.

In the previous article I covered building the Power App. In this article I will cover the Azure Machine Learning Studio Experiment.

We will build out this system in the following order; Power App, Azure Machine Learning, Power BI and then last MS Flow to connect the components. Before you can begin this tutorial there are some required prerequisites.

Prerequisites

  • Power Apps
  • MS Flow
  • Power BI Pro or Premium
  • Access to Azure Active Directory to register Power BI App
  • Dynamics 365

Build the Azure Machine Learning Experiment

The Azure Machine Learning Studio platform is a powerful cloud service from Microsoft that allows data scientists to rapidly build and deploy machine learning experiments. For the purpose of brevity, we will leverage an existing template from the Azure AI Gallery. The Azure AI Gallery is a great resource for creating and learning about Machine Learning experiments in the Microsoft platform.

Weehyong Tok from Microsoft created an experiment that segments customers based on the dataset Wholesale customers Data Set from UCI Machine Learning Repository which is perfect for our purposes.

You can find the experiment here https://gallery.azure.ai/Experiment/Customer-Segmentation-of-Wholesale-Customers-3 .

Open the experiment in the Azure Machine Learning Studio by clicking on Open in Studio. Be sure to log in using the same account that you used to build the Power App.

This will launch the Azure Machine Learning Studio platform and create an experiment for you based on Weehyong Tok template. You may notice that the experiment has to be updated, click ok.

This is because the Assign to Clusters module has been deprecated and replaced by a new module called Assign Data to Clusters. Thankfully the upgrade takes care of the necessary changes and we can use the experiment as is with out having to modify it.

Click the Run button at the bottom of the page.

Once the experiment has finished running click on the output of the Assign to Cluster module and select Visualize from the drop down menu.

As you can see in the image the data is grouped into clusters.

This experiment uses the K-Means clustering algorithm to assign the data points to groups. As you can see in the image below it currently uses 2 centroids which essentially means that each row will be assigned to 1 of 2 groups based on the distance of the data points in the row to the centroid.

Modify the experiment to determine the optimum number of centroids

Now you may wonder if this is the optimal number of clusters or not. Thankfully we can use an elbow chart to help determine the optimal number of centroids. To do this we will add a Python module to drop some code into our experiment.

Search for the Execute Python Script and drop it onto the canvas of the experiment. Connect the first output (the one on the left) of the Split Data module to the first input of the Execute Python Script module. Your experiment should look as follows.

Now you will need to add the following code to the Execute Python Script module. Replace the generated with the code below.

Python Code

# The script MUST contain a function named azureml_main
# which is the entry point for this module.

# imports up here can be used to 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from math import sin, cos, sqrt, atan2
from sklearn.cluster import KMeans
from sklearn import metrics
from scipy.spatial.distance import cdist

# The entry point function can contain up to two input arguments:
#   Param<dataframe1>: a pandas.DataFrame
#   Param<dataframe2>: a pandas.DataFrame
def azureml_main(dataframe1 = None, dataframe2 = None):

# Execution logic goes here
#print('Input pandas.DataFrame #1:\r\n\r\n{0}'.format(dataframe1)) #We don't need this, we just want the visual.

colors = ['b','g','r']
markers = ['o','v','s']

distortions = []
centroids = range(1, 10)
for i in centroids:
kmeanModel = KMeans(n_clusters=i).fit(dataframe1)
kmeanModel.fit(dataframe1)
distortions.append(sum(np.min(cdist(dataframe1, kmeanModel.cluster_centers_, 'euclidean'),axis=1))/dataframe1.shape[0])

plt.plot(centroids, distortions, 'bx-')
plt.xlabel('Number of centroids')
plt.ylabel('Distortions')
plt.title('Elbow chart showing the optimal number of centroids')
plt.show()

plt.savefig("elbow.png") #To see the chart in Azure Machine Learning Studio we need to save the image as a png.

# If a zip file is connected to the third input port is connected,
# it is unzipped under ".\Script Bundle". This directory is added
# to sys.path. Therefore, if your zip file contains a Python file
# mymodule.py you can import it using:
# import mymodule

# Return value must be of a sequence of pandas.DataFrame
return dataframe1,

Run the experiment and click on the second output, Python device (Dataset), of the Python Script module and select visualize. You should see something like the image below.

The optimal number of centroids is at the “elbow” of the chart above which looks to be about 5. Based on this insight we will update the algorithm and change the number of centroids to 5. We will also increase the number of iterations to 500 since we have more centroids.

Run the experiment and click on the output of the Assign to Cluster module and select Visualize from the drop down menu. The output should look like the image below.

Next, we will convert this experiment into a Predictive Web Service. At the bottom of the screen select Predictive Web Service > Predictive Web Service [Recommended]

Once the predictive experiment has been setup, we are going to modify it slightly so that it only returns the Assignment field. To do this we need to drop in the Select Columns in Dataset module and place it between the Assign to Clusters module and the Web service output.

Launch the column selector and enter in the Assignments column as the only value to get passed through to the web service output.

Run the experiment and Deploy Web Service.

This concludes the second part of this series. Next, we will build the API enabled dataset in Power BI which will store the data that we will use in the Power BI Reports and Dashboards. Since the dataset is API enabled we can push data into it using Flow.

Hopefully you have found this to be another practical post.

Until next time

Anthony

References

@Python Programming has a good site for understanding the Python code to plot an elbow chart.

https://pythonprogramminglanguage.com/kmeans-elbow-method/ 

 

Transforming data into value one blog post at a time

 

 

Thanks for joining me!

My name is Anthony Bulk and I am passionate about making data useful. Whether it’s through data storytelling, business intelligence, artificial intelligence or data storage I will cover it all.

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Last updated March 15, 2019.

Instant insight, automation and action using Power Apps, Power BI, Flow and Azure Machine Learning

Extend your information reach without over stretching by virtualizing data using SQL Server 2019 and MongoDB


“Without data you’re just another person with an opinion.”

W. Edwards Deming

 “If I had only one hour to save the world, I would spend fifty-five minutes defining the problem, and only five minutes finding the solution.”

Albert Einstein

Instant insights, automation and action – Part 1 Create Power App

In this series of blog posts, I will explain how you can integrate Power BI, Power Apps, Flow, Azure Machine Learning and Dynamics 365 to rapidly build a functioning system which allows users to analyze, insert, automate and action data. The tutorial will be premised on analyzing whole sale customer purchases using the Wholesale customers Data Set from UCI Machine Learning Repository.

The conceptual architecture of the system is illustrated below.

We will build out this system in the following order; Power App, Azure Machine Learning, Power BI and then last MS Flow to connect the components. Before you can begin this tutorial there are some required prerequisites.

Prerequisites

  • Power Apps
  • MS Flow
  • Power BI Pro or Premium
  • Access to Azure Active Directory to register Power BI App
  • Dynamics 365

Build the Power App

First we will build a very simple Power App. The app will allow users to enter in new purchase orders directly in a Power BI dashboard by leveraging the Power Apps custom Visual for Power BI.

Our app will have fields to capture the following data elements:

  • Customer Name
  • Channel
  • Region
  • Amount spent on FRESH produce
  • Amount spent on MILK produce
  • Amount spent on GROCERY produce
  • Amount spent on FROZEN product
  • Amount spent on DETERGENT and PAPER products
  • Amount spent on DELICASSEN products
  • Category number

The app will look as follows when complete.

Log onto Power Apps https://web.powerapps.com/home and select create new blank app. Select portrait layout.

This will open up a blank canvas. Your screen should look similar to the following image below.

Next we will add text input fields for each one of the data entry items listed above.

To do this navigate to Insert > Text > Text input.

Size the input field and enter in the appropriate name for the control, remove the default and add a text hint.

Repeat this for each data entry field.

When finished you should have a text input field for the following data elements:

  • NAME
  • CHANNEL
  • REGION
  • FRESH
  • MILK
  • GROCERY
  • FROZEN
  • DETERGENT
  • DELICASSEN
  • CATEGORY

Your app should now look like the following image.

Next we will add a button. To do this click on Insert > Button. Rename the button to SUBMIT and position it in the bottom right hand corner of the screen.

Your screen should now look as follows.

Save the app and give it an icon. I called mine the Customer Data Entry App.

This concludes the first part of this series. Next, we will build the Azure Machine Learning Studio experiment that we will use to categorize the customer if the customers category number has not been filled out in the app.

Hopefully you have found this to be another practical post.

Until next time

Anthony

References

@ChuckSterling has an excellent series of videos on embedding a Power App in a Power BI Dashboard.

https://www.youtube.com/watch?v=xKTPI2pEl9I

https://www.youtube.com/watch?v=dZb3vzp1WFE&list=WL&index=46&t=706s

@NathanPatrickTaylor also has a great video on integrating Power BI, Power Apps and Flow.

https://www.youtube.com/watch?v=au4a3AEIbKw&index=47&list=WL