Introduction Tooltips are a powerful feature in Power BI, offering additional context and information for visuals. However, tooltips are not natively supported for column headers in Power BI. This means users cannot view detailed information about column headers directly. Fortunately, there’s a workaround to address this limitation, which we’ll explore in this blog post. The method we are going to use is the Action buttons. We will add an action button to column headers, and the action button will have a tooltip that shows header details. Please find a step-by-step guide for the same – 1.       First, we need to insert a button. Here I have used a blank button since I want to show the tooltip when the user hovers through a particular column area, but you can use any of the buttons based on your choice. 2. Now, go to the button visual settings, turn on the action button, set the type to bookmark with None as an option, and specify the tooltip. Conclusion In this blog post, we discussed adding tooltips to the column headers to give users more context. we hope you found this blog post helpful! If you have any questions or want to discuss further, please contact us at transform@cloudfronts.com.

If you are working with SSRS reports in Dynamics 365 CRM, you may have to run or use the report for a single selected record, depending on your need. In this blog, I will explain how to run a report on a selected record in CRM. Running a report on a selected record is also referred to as prefiltering, so let’s first understand what prefiltering is in an SSRS Report.  What is Prefiltering in an SSRS Report?  Prefiltering in an SSRS Report is the process of applying filters before data is retrieved from the data source. It limits the dataset to include only the relevant records by incorporating conditions directly into the query or stored procedure used to fetch data for the report. It only shows relevant data that is needed for the reporting.  To add prefiltering to an SSRS report, follow these steps:  – First, identify the entity you want to run the report for. If the same report needs to be run on multiple entities in CRM, you will need to add the prefiltering condition to all required entities.   – For example, I have considered this project entity   <entity name=”msdyan_project” enableprefiltering=”1″ prefilterparametername=”Parameter1″>  enableprefiltering = “1” specifies that the data for ‘msdyan_project’ will be filter before report is shown for user.  – When running the above query, a parameter named “Parameter1” will be created automatically in Visual Studio.  – Now, publish the SSRS report to the desired environment and add the required field to the related project field to the related record type and you are all set.  Conclusion  Running an SSRS report on a single CRM record is an effective way to display relevant data while ensuring the report remains context sensitive. In this blog, we have provided a step-by-step guide to adding a prefilter in your SSRS report.  We hope you found this article useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com

Introduction Building efficient Power BI reports can be challenging, especially when working with large datasets. One common issue Power BI users encounter is the “stack overflow” error, which can disrupt the report-building process. I In this blog I will share some performance optimization techniques that you can use in building power BI report.  When using power query or importing data you might have got this error –  “Expression.Error: Evaluation resulted in a stack overflow and cannot continue.”  This error occurs when there’s a large amount of data is being imported or not enough memory available memory available for Power BI to complete the operation.  This issue can be resolved by increasing the Memory and CPU cores that can be used by Power BI while querying or evaluations.  There are two settings that we need to keep in mind –  By default, the maximum number of simultaneous evaluations is equal to the number of logical CPU cores on the machine and Maximum memory used per simultaneous evaluation is 432 MB.  Personally, I have kept these values in between or close to maximum value depending on my requirement and system.      Also, here is link to recommendations by Microsoft for managing Power BI workload and evaluation configurations –  https://learn.microsoft.com/en-us/power-bi/create-reports/desktop-evaluation-configuration  Conclusion Optimizing performance in Power BI is crucial for handling large datasets and preventing issues like the “stack overflow” error. By adjusting settings for simultaneous evaluations and memory allocation, you can significantly improve report processing and responsiveness. We hope you found this article useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com

Introduction  In this blog post, I’ll walk you through the process of transforming JSON to JSON using Azure Logic Apps and the Liquid Template Language. This step-by-step guide will demonstrate how you can use Azure Integration Services to achieve your transformation goals.                                      What is Liquid Template Language?  The Liquid Template Language (commonly referred to as “Liquid”) is a flexible, open-source template language developed by Shopify. It is widely used to render dynamic content in platforms such as Shopify themes, Jekyll websites, and web applications. Liquid uses placeholders, loops, and conditional statements to pull dynamic data into a web template, making it an effective tool for JSON transformation.  Prerequisites  To complete this tutorial, you’ll need:  Sample Input JSON  We will use the following sample JSON file for this tutorial:  {    “FirstName”: “Deepak”,    “LastName”: “Ch”,    “Add1”: “T square, Saki Vihar Road, Andheri East”,    “Add2”: “Mumbai”,    “Landmark”: “Near Car Showroom”,    “PhoneNo1”: 9812727261,    “PhoneNo2”: 2121233322  }  Desired Output JSON  The client’s requirement is to transform the input JSON into the following format:  {    “Full Name”: “Deepak Ch”,    “Address”: “T square, Saki Vihar Road, Andheri East, Mumbai, Near Car Showroom”,    “Phone”: “9812727261, 2121233322”  }  Step-by-Step Guide –   Step 1: Create a Free Azure Integration Account  Step 2: Add the Liquid Template Map  Step 3: Create a Logic App  Step 4: Transform JSON to JSON using Liquid  Here’s the Liquid template used for this transformation:  {    “Full Name”: “{{content.FirstName}} {{content.LastName}}”,    “Address”: “{{content.Add1}}, {{content.Add2}}, {{content.Landmark}}”,    “Phone”: “{{content.PhoneNo1}}, {{content.PhoneNo2}}”  }      Step 5: Test with Postman  Final Output  The output JSON will be:  {    “Full Name”: “Deepak Ch”,    “Address”: “T square, Saki Vihar Road, Andheri East, Mumbai, Near Car Showroom”,    “Phone”: “9812727261, 2121233322”  }  We hope you found this article useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com

In continuation to our Part 1, welcome to part 2 of the blog on Automating Access Token and Refresh Token Generation Using ADF and Azure Key Vault. We have already completed the necessary setup in part 1, so if you haven’t read part 1 yet, please do so before proceeding with this part.  Assumptions-  Before going further, let’s first discuss the assumptions we made:  Now, let’s discuss the step to create a pipeline to refresh the access token: –   – Create a web activity to pull the client ID, client secret, and refresh token you created in part 1. – As for settings, you use this setup, and URI is your Azure key vault’s Secret Identifier.  – Similarly, set up web activities for the client ID, client secret, and refresh token.  – For the refresh token, I have done setup as shown but you may want to change it according to your API requirements.  Body-   grant_type=refresh_token&refresh_token=@{activity(‘Get Refresh Token’).output.value}  Authorization-   Basic @{base64(concat(activity(‘Get Client Id’).output.value, ‘:’, activity(‘Get Client Secret’).output.value))}  – After this, use another web activity to refresh the access token using the refresh token and save it to the Azure Key Vault.  Body-  {    “value”: “@{activity(‘Refresh Access Token’).output.access_token}”  }  Conlusion: This blog provides a comprehensive guide to automating the access token and refresh token generation process using Azure Data Factory and Azure Key Vault. By following the steps outlined, you can ensure seamless token management, reduce manual interventions, and maintain secure access to your resources. We hope you found this article useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com

Introduction In this blog, I will explain how we can automate generating access tokens or refresh tokens. When working with APIs, a common problem is the expiration of the access token or refresh token after some time. We solved this issue by using Azure Data Factory and Azure Key Vault.  Azure Key Vault is used for storing API credentials as it is one of the most secure ways to store keys/secrets in Azure. Azure Data Factory is used to automate the process of generating access tokens for APIs. We are dividing this blog into two parts:  Before we proceed with the blog, please test your API in Postman to know the API requirements for generating access tokens. For me, it is client ID, client secret, and Refresh Token.  Steps to Set Up Azure Key Vault and Azure Data Factory:  – Go to the Azure portal and create a Key Vault resource. Please make sure that your Key Vault and Azure Data Factory are in the same region.    – Create a secret by generate/import and entering the required details. I have already created the secrets I need. – For the access token, you can keep the initial value as anything you want; we will update it later using an ADF Pipeline.   – Set up an access policy for the Azure Data Factory to access our Key Vault. To do this, go to “Access Policy” and select the appropriate options.   – Click “Next” and select your Azure Data Factory, where you will be creating a pipeline for refreshing the access token.   – Now, go to Azure Data Factory Studio and set up the linked Service for your API in the Azure data factory.  – The dataset is also pretty straightforward, and I prefer to use a parameter for the relative URL so that I can reuse the same dataset and just set the URL of the API I want to call during runtime:  Conclusion That’s all for the setup in part 1. We’ve covered the essential steps to set up Azure Key Vault and Azure Data Factory for securely managing API credentials and setting the groundwork for automating access token generation. These tools provide a reliable and secure way to handle token expiration, ensuring smooth API operations without manual intervention. In part 2, we will discuss in detail how we can automate access token generation using Azure Data Factory. We hope you found this article useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com