Dynamics 365 and Power Platform monthly reading list April 2022

 

2022 Release Wave 1

• Duplicate detection for leads (Preview) by @D365Goddess
• Updates to the Power Platform Admin Center
• D365 Customer Service: Swarming using Teams (Preview)
• The new AADUser Virtual Table (Youtube)
• React (virtual) and Fluent UI code components using platform libraries

Technical topics (Configuration, customization and extensibility)

• Enhanced user experience with new modern control on subject field
• Customizing the side panel in the deal manager workspace
• Disable hyperlinks for lookup columns in grid views of model driven apps by @linnzawwin
• Dataverse's planet scale infrastructure by Power CAT Live (Youtube) by @topness
• Power Platform and Dynamics 365 Technical Update Briefing - March 2022 from @PFEDynamics
• Dataverse: improve CRUD performance using partition key
• Setup Postman to connect to Dynamics 365 CRM using oAuth 2.0 | Azure App Registration by @priyesh_wngman7
• Integration patterns in Dynamics 365 Finance & Operations
• ALM Power Platform Part 1 - Overview and Process
• Solution deployments: automated vs manual
• The curious case of the missing application user view (Dynamics 365/Dataverse)
• Install Power Apps for Windows (preview) - run model-driven or canvas apps on your Windows device.

Topics for Dynamics 365 Business Applications Platform consultants, project managers and power users

• What KPIs should service-based businesses monitor to ensure lasting success
• Evaluating software with Microsoft's Catalyst Framework
• The limits of legacy campaign management tools
• 4 ways to accelerate revenue with Dynamics 365 Sales and AI
• Customer case: Siemen's bold choice to pursue CRM transformation leads to unique collaborative results
• Power Platform - rise of the business technologists

Reading and writing files in Azure Blob Storage with Python

Azure Blob storage is Microsoft's object storage solution for the cloud and allows you to store massive amounts of unstructured data, such as text or binary data at low cost for every scale. If you are not familiar with it, I can recommend taking a look at the Store data in Azure learning path on Microsoft Learn

Using Python in combination with Azure Blob Storage is quite easy using the azure-storage-blob client library for Python . You can set up a container with private access meaning that you will need to provide credentials to access the containers and the blobs contained within. The easiest way to do this is using a shared access signature (SAS) token. You can generate a SAS token from the Azure Portal.

To interact with the different parts of Azure  Blob Storage you will typically use the BlobServiceClient to work with the Azure storage account itself, the ContainerClient to work with a specific container and the BlobClient to work with a specific blob.  Below is the sample code which uses these different clients in a Jupyter notebook (based on Quickstart: Manage blobs with Python v12 SDK)   - you can find the full Jupyter notebook at tradingnotebooks/AzureBlobStorage.ipynb at master · jorisp/tradingnotebooks (github.com) 

 References:

• Quickstart: Manage blobs with Python v12 SDK
• Using your data lake as a cheap time series database: do's and don'ts
• How to download blobs from Azure Storage using Python - sample code for multithreading