Enhancing Power BI Data Analysis with OpenAI API: A Follow-Up Guide


In our previous blog post, we walked you through the process of integrating Power BI with OpenAI’s GPT-4 to generate textual insights. This time, we will dive deeper into leveraging the OpenAI API to analyze data in Power BI and enhance your data-driven decision-making process. By incorporating OpenAI’s natural language processing capabilities, you can extract valuable information from textual data and make informed decisions based on your Power BI analysis.


  1. Familiarity with the previous blog post on integrating Power BI with OpenAI
  2. A Power BI Pro or Premium account
  3. OpenAI API Key
  4. Python and required libraries installed (openai, pandas, powerbiclient)

Note – Please be aware that this solution involves interacting with OpenAI’s API. I encourage users to familiarize themselves with OpenAI’s data usage policy (https://platform.openai.com/docs/data-usage-policy) and take necessary precautions to ensure the privacy and security of their data.

Step 1: Fetch and Process Textual Data

Before analyzing textual data with the OpenAI API, you need to fetch and preprocess it. In this example, we will work with customer reviews data. You can import this data into Power BI from various sources like a CSV file, Excel file, or a database.

  1. Import customer reviews data into Power BI and create a dataset.
  2. Clean and preprocess the data as necessary (e.g., removing duplicates, handling missing values, etc.).

Step 2: Update the Python Script to Analyze Textual Data with OpenAI

  1. Open the Python script you created in the previous blog post (e.g., ‘openai_powerbi_integration.py’) and locate the fetch_openai_data function.
  2. Modify the function to accept the text to be analyzed as an input parameter:
def fetch_openai_data(text):

3. Update the OpenAI API call within the function to perform the desired analysis task. For example, you can modify the prompt to perform sentiment analysis:

def fetch_openai_data(text):
    prompt = f"Analyze the sentiment of the following customer review: '{text}'. Is it positive, negative, or neutral?"
    response = openai.Completion.create(
    generated_text = response.choices[0].text.strip()
    return generated_text

Step 3: Analyze Textual Data in Power BI Using the Updated Python Script

  1. In Power BI, create a new column in the customer reviews dataset to store the sentiment analysis results.
  2. Iterate through the customer reviews and call the fetch_openai_data function for each review. Store the sentiment analysis result in the new column:
for index, row in customer_reviews.iterrows():
    text = row["Review_Text"]
    sentiment = fetch_openai_data(text)
    customer_reviews.loc[index, "Sentiment"] = sentiment

Step 4: Visualize the Analyzed Data in Power BI

  1. In Power BI, create a new report using the updated customer reviews dataset.
  2. Design visualizations to display the sentiment analysis results, such as pie charts, bar charts, or word clouds. You can also create filters to allow users to interact with the data and explore specific segments, such as reviews with positive or negative sentiment.
  3. Save and publish the report to share the AI-enhanced insights with your team.


In this follow-up blog post, we demonstrated how to use the OpenAI API to analyze textual data in Power BI, enhancing your data analysis capabilities. By incorporating the power of OpenAI’s natural language

processing into your Power BI dashboard, you can gain deeper insights and make more informed decisions based on your data.

Remember that this is just one example of how you can integrate OpenAI with Power BI for data analysis. You can customize the Python script and the OpenAI prompt to perform other analysis tasks such as topic extraction, keyword identification, or summarization. The possibilities are vast, and with a little creativity, you can unlock the full potential of combining AI and business intelligence tools to drive your organization’s success.

As you continue exploring the integration of OpenAI and Power BI, always keep in mind the ethical considerations and usage guidelines of AI-generated content. Ensure that the generated insights align with your organization’s values and goals while adhering to responsible AI practices.

Learn More

If you’re interested in learning more, here are three example follow-up questions you could ask ChatGPT about the blog post:

  1. In the blog post about using the OpenAI API to analyze data in Power BI, how can I optimize the Python script to handle a large volume of textual data for analysis without exceeding API rate limits or incurring excessive costs?
  2. What are some methods to ensure data privacy and security when analyzing sensitive textual data, such as customer reviews or internal communications, using the OpenAI API and Power BI integration?
  3. Are there any limitations or potential biases in the AI-generated analysis that users should be aware of when interpreting the results and making data-driven decisions based on the Power BI visualizations?

This blogpost was created with help from ChatGPT Pro.

Integrating Power BI with OpenAI: A Comprehensive Guide


As the need for data-driven decision-making grows, integrating artificial intelligence (AI) with business intelligence (BI) tools has become an invaluable asset for businesses. Two popular tools in these fields are Power BI by Microsoft and OpenAI’s GPT-4. In this blog post, we’ll walk you through a detailed process to integrate Power BI with OpenAI, unlocking powerful analytics and AI capabilities for your organization.

Power BI is a suite of business analytics tools that helps you visualize and share insights from your data. OpenAI is a cutting-edge AI research lab that has developed the GPT-4, a large language model capable of understanding and generating human-like text. By integrating Power BI with OpenAI, you can enhance your data analysis and generate insights using AI techniques.

Note – Please be aware that this solution involves interacting with OpenAI’s API. I encourage users to familiarize themselves with OpenAI’s data usage policy (https://platform.openai.com/docs/data-usage-policy) and take necessary precautions to ensure the privacy and security of their data.


  1. A Power BI Pro or Premium account
  2. OpenAI API Key (Sign up for OpenAI’s API service at https://beta.openai.com/signup/)

Step 1: Install Python and Required Libraries

To install Python on your computer, follow these steps:

  1. Visit the official Python website: https://www.python.org/downloads/
  2. You will see the download buttons for the latest Python version available for your operating system (Windows, macOS, or Linux). Click on the appropriate button to download the installer. If you need a different version or want to explore other installation options, click on the “View the full list of downloads” link below the buttons.
  3. Once the installer is downloaded, locate the file in your downloads folder or wherever your browser saves downloaded files.
  4. Run the installer by double-clicking on the file.

For Windows:

  • In the installer, check the box that says “Add Python to PATH” at the bottom. This will allow you to run Python from the command prompt easily.
  • Select the “Customize installation” option if you want to change the default installation settings, or just click on “Install Now” to proceed with the default settings.
  • The installer will install Python and set up the necessary file associations.

For macOS:

  • Follow the installation prompts in the installer.
  • Depending on your macOS version, Python may already be pre-installed. However, it’s usually an older version, so it’s still recommended to install the latest version from the official website.

For Linux:

  • Most Linux distributions come with Python pre-installed. You can check the installed version by opening a terminal and typing python --version or python3 --version. If you need to install or update Python, use your distribution’s package manager (such as apt, yum, or pacman) to install the latest version.

After installation, open a command prompt or terminal and type python --version or python3 --version to ensure that Python has been installed correctly. You should see the version number of the installed Python interpreter.

Now you have Python installed on your computer and are ready to proceed with installing the required libraries and running Python scripts.

2. Install the following Python libraries using pip:

pip install openai pandas powerbiclient
pip install msal

Step 2: Create a Power BI Dataset and Table

  1. Sign in to Power BI service at https://app.powerbi.com/
  2. Navigate to your workspace and click on ‘Datasets + dataflows’ in the left pane.
  3. Click on the ‘+ Create’ button and select ‘Streaming Dataset.’
  4. Choose ‘API’ as the connection type and click ‘Next.’
  5. Provide a name for your dataset, such as ‘OpenAI_Insights,’ and click ‘Create.’
  6. You will receive an API URL and an authentication token. Save these for later use.

Step 3: Create a Python Script to Fetch Data and Push to Power BI

  1. Create a new Python script (e.g., ‘openai_powerbi_integration.py’) and import the required libraries:
import openai
import pandas as pd
from powerbiclient import Report, models
import requests
from msal import PublicClientApplication

2. Set up OpenAI API and Power BI authentication:

# Set up OpenAI API
openai.api_key = "your_openai_api_key"

# Set up Power BI authentication
POWER_BI_CLIENT_ID = "your_power_bi_client_id"
AUTHORITY = "https://login.microsoftonline.com/common"
SCOPE = ["https://analysis.windows.net/powerbi/api/.default"]
app = PublicClientApplication(POWER_BI_CLIENT_ID, authority=AUTHORITY)

result = None
accounts = app.get_accounts()
if accounts:
    result = app.acquire_token_silent(SCOPE, account=accounts[0])

if not result:
    flow = app.initiate_device_flow(scopes=SCOPE)
    result = app.acquire_token_by_device_flow(flow)

powerbi_auth_token = result["access_token"]

Replace your_openai_api_key with your OpenAI API key and your_power_bi_client_id with your Power BI client ID. The script will prompt you to authenticate with Power BI by providing a URL and a device code.

To obtain your Power BI client ID, you need to register an application in the Azure Active Directory (Azure AD) associated with your Power BI account. Here’s a step-by-step guide to help you get your Power BI client ID:

  1. Sign in to the Azure portal: Go to https://portal.azure.com/ and sign in with the account you use for Power BI.
  2. Navigate to Azure Active Directory: Once you’re logged in, click on “Azure Active Directory” from the left-hand menu or find it using the search bar at the top.
  3. Register a new application: In the Azure Active Directory overview page, click on “App registrations” in the left-hand menu, and then click on the “+ New registration” button at the top.
  4. Configure your application:
    • Provide a name for your application (e.g., “PowerBI_OpenAI_Integration”).
    • Choose the supported account types (e.g., “Accounts in this organizational directory only” if you want to restrict access to your organization).
    • In the “Redirect URI” section, choose “Web” and provide a URL (e.g., “https://localhost“). This is just a placeholder and won’t be used for our Python script.
  5. Click on the “Register” button at the bottom to create the application.
  6. Obtain your client ID: After registering your application, you’ll be redirected to the application’s overview page. Here, you’ll find the “Application (client) ID.” This is the Power BI client ID you need for your Python script. Make sure to copy and save it securely.
  7. Grant API permissions:
    • In the application’s main menu, click on “API permissions.”
    • Click on the “+ Add a permission” button and select “Power BI Service.”
    • Choose “Delegated permissions” and check the “Dataset.ReadWrite.All” permission.
    • Click on the “Add permissions” button to save your changes.
  8. Don’t forget to update the Power BI client ID in your Python script!

3. Create a function to fetch data from OpenAI and process it:

def fetch_openai_data(prompt):
response = openai.Completion.create(
generated_text = response.choices[0].text.strip()
return generated_text

4. Create a function to push data to Power BI:

def push_data_to_powerbi(api_url, auth_token, dataframe):
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {auth_token}",
data_json = dataframe.to_json(orient="records")
response = requests.post(api_url, headers=headers, data=data_json)
return response.status_code

5. Use the functions to fetch data from OpenAI and push it to Power BI:

# Define your OpenAI prompt
prompt = "Summarize the key factors affecting the global economy in 2023."

# Fetch data from OpenAI
openai_data = fetch_openai_data(prompt)

# Create a DataFrame with the data
data = {"OpenAI_Insight": [openai_data]}
dataframe = pd.DataFrame(data)

# Push data to Power BI
api_url = "your_power_bi_api_url"
auth_token = powerbi_auth_token
status_code = push_data_to_powerbi(api_url, auth_token, dataframe)

# Print status code for confirmation
print(f"Data push status code: {status_code}")

6. Save and run the Python script:

python openai_powerbi_integration.py

If successful, you should see the status code ‘200’ printed, indicating that the data push was successful.

Step 4: Create a Power BI Report and Visualize Data

  1. Go back to Power BI service and navigate to the ‘OpenAI_Insights’ dataset.
  2. Click on the dataset to create a new report.
  3. In the report editor, create a table or any other visualization type to display the ‘OpenAI_Insight’ data.
  4. Save and publish the report to share insights with your team.


In this blog post, we walked you through the process of integrating Power BI with OpenAI. By following the steps, you can use Power BI to visualize and share insights generated by OpenAI’s GPT-4, enhancing your data analysis capabilities. This integration opens up new possibilities for advanced data-driven decision-making, enabling your organization to stay ahead of the competition.

Remember that this is just a starting point. You can further customize the Python script to fetch and process more complex data from OpenAI, and even create dynamic, real-time dashboards in Power BI to keep your team updated with the latest AI-generated insights.

Learn More

If you’re interested in learning more, here are three example follow-up questions you could ask ChatGPT about the blog post:

  1. How can I customize the OpenAI prompt to generate more specific insights or analyses for my Power BI dashboard?
  2. What are some best practices for visualizing the AI-generated insights in Power BI to create effective and easy-to-understand reports?
  3. Can you provide examples of other use cases where the integration of Power BI and OpenAI can be beneficial for businesses or organizations?

Make sure you provide the context of the blogpost when asking your follow-up questions. Here is an example of how you could ask it:

In the blog post about integrating Power BI with OpenAI, you mentioned creating a Python script to fetch data and push it to Power BI. How can I customize the OpenAI prompt within the script to generate more specific insights or analyses for my Power BI dashboard?

Thanks for reading!

This blogpost was created with help from ChatGPT Pro.

Unraveling the Power of the Spark Engine in Azure Synapse Analytics


Azure Synapse Analytics is a powerful, integrated analytics service that brings together big data and data warehousing to provide a unified experience for ingesting, preparing, managing, and serving data for immediate business intelligence and machine learning needs. One of the key components of Azure Synapse Analytics is the Apache Spark engine, a fast, general-purpose cluster-computing system that has revolutionized the way we process large-scale data. In this blog post, we will explore the Spark engine within Azure Synapse Analytics and how it contributes to the platform’s incredible performance, scalability, and flexibility.

The Apache Spark Engine: A Brief Overview

Apache Spark is an open-source distributed data processing engine designed for large-scale data processing and analytics. It offers a high-level API for parallel data processing, making it easy for developers to build and deploy data processing applications. Spark is built on top of the Hadoop Distributed File System (HDFS) and can work with various data storage systems, including Azure Data Lake Storage, Azure Blob Storage, and more.

Key Features of the Spark Engine in Azure Synapse Analytics

  1. Scalability and Performance

The Spark engine in Azure Synapse Analytics provides an exceptional level of scalability and performance, allowing users to process massive amounts of data at lightning-fast speeds. This is achieved through a combination of in-memory processing, data partitioning, and parallelization. The result is a highly efficient and scalable system that can tackle even the most demanding data processing tasks.

  1. Flexibility and Language Support

One of the most significant advantages of the Spark engine in Azure Synapse Analytics is its flexibility and support for multiple programming languages, including Python, Scala, and .NET. This allows developers to use their preferred programming language to build and deploy data processing applications, making it easier to integrate Spark into existing workflows and development processes.

  1. Integration with Azure Services

Azure Synapse Analytics provides seamless integration with a wide range of Azure services, such as Azure Data Factory, Azure Machine Learning, and Power BI. This enables users to build end-to-end data processing pipelines and create powerful, data-driven applications that leverage the full potential of the Azure ecosystem.

  1. Built-in Libraries and Tools

The Spark engine in Azure Synapse Analytics includes a rich set of built-in libraries and tools, such as MLlib for machine learning, GraphX for graph processing, and Spark Streaming for real-time data processing. These libraries and tools enable developers to build powerful data processing applications without the need for additional third-party software or libraries.

  1. Security and Compliance

Azure Synapse Analytics, along with the Spark engine, offers enterprise-grade security and compliance features to ensure the protection of sensitive data. Features such as data encryption, identity and access management, and monitoring tools help organizations maintain a secure and compliant data processing environment.


The Spark engine in Azure Synapse Analytics plays a crucial role in the platform’s ability to deliver exceptional performance, scalability, and flexibility for large-scale data processing and analytics. By leveraging the power of the Spark engine, organizations can build and deploy powerful data processing applications that take full advantage of the Azure ecosystem. In doing so, they can transform their data into valuable insights, driving better decision-making and ultimately leading to a more successful and data-driven organization.

This blogpost was created with help from ChatGPT Pro.

Harnessing the Power of Azure Synapse Spark and Power BI Paginated Reports: A Comprehensive Walkthrough

In today’s data-driven world, organizations seek to harness the vast potential of their data by combining powerful technologies. Azure Synapse Spark, a scalable data processing engine, and Power BI Paginated Reports, a robust report creation tool, are two such technologies that, when combined, can elevate your analytics capabilities to new heights.

In this blog post, we’ll walk you through the process of integrating Azure Synapse Spark with Power BI Paginated Reports, enabling you to create insightful, flexible, and high-performance reports using big data processing.


Before we begin, ensure you have the following set up:

  1. An Azure Synapse Workspace with an Apache Spark pool.
  2. Power BI Report Builder installed on your local machine.
  3. A Power BI Pro or Premium subscription.

Step 1: Prepare Your Data in Azure Synapse Spark

First, you’ll need to prepare your data using Azure Synapse Spark. This involves processing, cleaning, and transforming your data so that it’s ready for use in Power BI Paginated Reports.

1.1. Create a new Notebook in your Synapse Workspace, and use PySpark, Scala, or Spark SQL to read and process your data. This could involve filtering, aggregating, and joining data from multiple sources.

1.2. Once your data is processed, write it to a destination table in your Synapse Workspace. Ensure that you save the data in a format compatible with Power BI, such as Parquet or Delta Lake.

Step 2: Connect Power BI Paginated Reports to Azure Synapse Analytics

With your data prepared, it’s time to connect Power BI Paginated Reports to your Azure Synapse Analytics.

2.1. Launch Power BI Report Builder and create a new paginated report.

2.2. In the “Report Data” window, right-click on “Data Sources” and click “Add Data Source.” Select “Microsoft Azure Synapse Analytics” as the data source type.

2.3. Enter your Synapse Analytics server name (your Synapse Workspace URL) and database name, then choose the appropriate authentication method. Test your connection to ensure it’s working correctly.

Step 3: Create a Dataset in Power BI Report Builder

Now that you’re connected to your Synapse Workspace, you’ll need to create a dataset in Power BI Report Builder to access the data you prepared earlier.

3.1. In the “Report Data” window, right-click on “Datasets” and select “Add Dataset.”

3.2. Choose the data source you created earlier, then write a query to retrieve the data from your destination table in Synapse Workspace. You can use either SQL or the Synapse SQL provisioned pool for this task. Test the query to ensure it retrieves the data correctly.

Step 4: Design Your Power BI Paginated Report

With your dataset ready, you can start designing your Power BI Paginated Report.

4.1. Drag and drop the appropriate data regions, such as tables, matrices, or lists, onto the report canvas.

4.2. Map the dataset fields to the data region cells to display the data in your report.

4.3. Customize the appearance of your report by applying styles, formatting, and conditional formatting as needed.

4.4. Set up headers, footers, and pagination options to ensure your report is well-organized and professional.

Step 5: Test, Export, and Share Your Report

The final step in the process is to test, export, and share your Power BI Paginated Report.

5.1. Use the “Preview” tab in Power BI Report Builder to test your report and ensure it displays the data correctly

5.2. If you encounter any issues, return to the design view and make any necessary adjustments.

5.3. Once you’re satisfied with your report, save it as a .rdl file.

5.4. To share your report, publish it to the Power BI Service. Open the Power BI Service in your browser, navigate to your desired workspace, click on “Upload,” and select “Browse.”

5.5. Upload the .rdl file you saved earlier, and wait for the publishing process to complete.

5.6. After your report is published, you can share it with your colleagues, either by granting them access to the report in the Power BI Service or by exporting it to various formats, such as PDF, Excel, or Word.


By combining the processing power of Azure Synapse Spark with the flexible reporting capabilities of Power BI Paginated Reports, you can create insightful, performant, and visually appealing reports that leverage big data processing. The walkthrough provided in this blog post offers a step-by-step guide to help you successfully integrate these two powerful tools and unlock their full potential. As you continue to explore the possibilities offered by Azure Synapse Spark and Power BI Paginated Reports, you’ll undoubtedly uncover new ways to drive your organization’s data-driven decision-making to new heights.

This blogpost was created with help from ChatGPT Pro.

How to use A SKU’s to try out Paginated Reports in Power BI without upfront cost or long term commitment


Since the Eagles game doesn’t start until later, thought I’d put this together for folks.

As many of you know, earlier this month we announced a preview of Paginated Reports in Power BI.  While folks were excited about this, there were those who were disappointed it was only available (for now) if you had Power BI Premium.

“I don’t have Premium (yet . . .),” they said,  “But I still want to try this out and use it.  How can I do so without committing to it for a month and paying $5000?”

The good news is, there is an easy way to do this.  See, as I’ve stated in previous interviews, you can try all the functionality out by spinning up an Power BI Embedded A SKU capacity, which is available to purchase through Microsoft Azure.  While normally A SKU’s are specifically used for embedding scenarios, there is no licensing restriction against using them internally if you’d like.  However, generally this makes little sense vs. purchasing a P SKU for most use cases, since each user would still need a Pro license to access the Power BI portal AND it’s almost $1000 more per month if you run it all the time.

But if you just want to try out the new functionality that is available only on Premium capacity, the big advantage of A SKU’s is you can stop/start them just like any VM in Azure, and you’re billed by the minute vs. having an initial upfront monthly cost.  This means you can spin up an A SKU, turn on the paginated reports capability in your Power BI portal, and start using it.  And when you’re done, you can go into Azure and pause the capacity until you want to use it again.  Since you’re only billed for the time it’s running, you can try this new functionality out for around 14 cents a minute! (this figure is based on my fuzzy math being done while my son is playing Fortnite right next to me, so forgive me if this is off by a few cents one way or another).  Let’s walk through how you’d go set this up yourself –

I have my own Power BI subscription (yes, I pay out of pocket for this), since I like to have access to the exact same experience as all of my users do (or I forgot to turn it off when I joined the team . . .).  In any event, I have a Pro license, but when I check the Admin Portal, I can see I have no Premium capacities right now.


Note another tab there which says “Power BI Embedded” – I don’t have any of those capacities, which you purchase in Azure, spun up currently either.


To get one of those going, I’m going to head to the Azure Portal and login with my credentials that I also use for Power BI.  Why? Because I’m the only user, so I’m also the admin.  Now, I can do a search for “Power BI Embedded”.  This will take me to the management page, which looks like this


I hit the “Create Power BI Embedded” button, which, if you haven’t signed up for an Azure account yet, you’ll be prompted to do so, and this includes a $200 credit for 30 days.  If you have signed up for Azure, you will skip this step (duh).


Now that I have that setup, I’ll go back to the portal and get back to the previous screen.  Here, I can click the button again to setup a new Power BI Embedded A SKU.  You need to select at least an A4 capacity size or higher to use Paginated Reports, so I’ll pick an A4 in my home location to spin up.  (NOTE: Paginated Reports are also supported in multi-geo scenarios, even during our public preview, so I could choose other regions outside my home region if I wanted to.)


Everything looks good, so I’ll hit create and wait for it to finish deploying.


If I check my Power BI Portal, I see that it is also showing as being deployed there under the Capacity Settings –> Power BI Embedded tab

Once it’s completed deployment, when I click on the capacity name, I’ll see I can manage this capacity just like I would any premium capacity in Power BI.


Under the Workloads, I see the two new preview workloads, and I’ll set Paginated Reports to “On” and assign 50% of the memory to that workload.  I can also try out the new Dataflows workload as well, but I’ll save that for another time.


After a few moments, I’ll see the message has changed from starting to Ready, and my workload is now ready to use.  I’ll assign all (one) of the workspaces for my organization to this new capacity.


Cool!  Now, I can upload my first paginated report to the workspace and view it.  I’m using a vintage Halo sample report, and it renders without a hitch.


But now I want to stop using the capacity and not get charged (since I finished this blog post).  No problem – I can go back to the Azure portal and just pause the capacity until I want to use it again.


When I’ve paused it, I can no longer view my paginated reports in Power BI, but they aren’t deleted or otherwise affected.  They’re still there waiting to be used again when the capacity is started up, and I can delete them if I need to.


And that’s it – in less than an hour, including the time it took to type this blogpost, I created a new Azure subscription, created my first Power BI “Embedded” A4 capacity, turned on the Paginated Report workload, assigned a workspace, uploaded and viewed my report, and then paused the capacity to stop the billing on it.  Whew!

Thanks so much for reading through the post today, and I hope you all take some time to try out the new paginated reports in Power BI Premium.  And if your organization doesn’t already have Power BI Premium, use this walkthrough to give it a try yourself!

Happy Thanksgiving (week)!

Adventure Works brand package now available!


Super short post today.

With the release of an updated test/demo Azure VM for Power BI Report Server on Monday, we thought it made sense to provide a new Adventure Works brand package for everyone to use in their demos/presentations to go along with it.  Feel free to download and use with Reporting Services 2016/2017 or Power BI Report Server – https://1drv.ms/u/s!Au6-0xX27UdgnOIapntXPaoPcqqOWQ


Updated demo VM for Power BI Report Server now available!


For those of you who enjoy using the test/dev/demo VM template the team has in Azure for Power BI Report Server, we’ve gone ahead and updated it with the latest preview we released a few weeks back.  With that update, we’ve made a few additional changes to the template as well –

– Office Online Server is now automatically installed and configured for you when you spin up a new environment.  There is also an Excel workbook available as one of the sample reports. (Please note – we do NOT currently setup an Analysis Services instance running in PowerPivot mode for you to use with the VM.  You can do that manually if you’d like by using the SQL install media already on the VM.)

– We now deploy two VM’s and setup a sample domain for them to allow you to use Office Online Server.  They will both be the same size you select in the VM setup process initially – you may re-size them if you’d like through the Azure portal at any time.  We did this to allow the widest number of regions access to the VM template, since not every VM size is available in every region.  This should help make sure you don’t accidentally select a VM that isn’t available in your region, and have the deployment fail for you. 

– We’ve added additional sample RDL reports with the VM for your use.

You can get started with the template in Azure here –http://bit.ly/2f2aWyn

I hope you find the latest updates valuable, and thanks for reading as always!