Part 1: Integrating AWS Data into Dataverse via Virtual Table
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Microsoft Dataverse
Sep 26, 2023 1:50 PM

Part 1: Integrating AWS Data into Dataverse via Virtual Table

by HubSite 365 about Michael Megel

Enterprise Architect, Azure DevOps, Power Platform Addict, Cloud Solutions & Intelligent ERP ... Never stop learning!

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Discover AWS Data integration in Microsoft Dataverse utilizing Virtual Tables and connectors - a comprehensive guide from a Microsoft expert.

A Summary of the Blog Post on AWS Data Integration with Microsoft Dataverse using Virtual Tables – Part 1

The author of the post investigates the potential for data integration between Microsoft Dataverse and AWS, two data stores from different cloud service providers. Data becomes vital in modern digital enterprises where several systems may often be needed to make a decision. Microsoft Dataverse is a tool that is suitable for this task due to its ability to be connected to other data sources via over 800 connectors as well as an additional feature called virtual tables.

Virtual tables in Microsoft Dataverse allow for the integration and access of data from alternate platforms. The author previously described in an earlier blog post (Business Central in Dataverse as Virtual Tables) how simple it is for business data from Microsoft Dataverse to be integrated. Unlike connectors, virtual tables can also be used in model-driven apps, forms, and views. In this article, the author delves into how external data can be integrated as a virtual table within Microsoft Dataverse.

The author briefly discussed AWS and how data can exist outside of the Microsoft ecosystem. An AWS DynamoDB, similar to Microsoft's Azure Cosmos DB, is a versatile NoSQL database service suitable for any scale. The main issue is how to gain access to this data from Microsoft Dataverse.

Integration Steps and Challenges

Creating a data source for Dataverse's virtual tables led to a series of steps and challenges. The author went into his Dataverse solution and attempted to add an external table from non-existent data. The limited options of SQL Server and SharePoint were not what he anticipated. The solution he sought was Business Central’s APIs, which are leveraged by the Business Central Virtual Table app to structure virtual tables on Microsoft Dataverse.

The author navigates to the advanced settings to find Virtual Entity Data Sources. Here, he managed to create a data source for his DynamoDB via an AWS DynamoDB adapter for Amazon Connect. The next step takes him over to AWS to create an OData Endpoint.

In AWS, the author used Code Catalyst to create an endpoint with a .NET serverless application. This application included an OData API hosted on AWS Lambda Function and presented through an AWS API Gateway. The utilization of ASP.NET Core C# coupled with a few additional packages allowed the author to successfully implement a very concise data model relating to a single class “ProductCategory”. This simplified class model forms the gist of the blog post.

The final part of the process in AWS involves creating an Entity Data Model (EDM) which is essentially a blueprint that allows Microsoft Dataverse to interact with the service endpoints. The author realizes this and so goes into increasing detail about the creation process involving XML coding to achieve his objective. The blog post then concludes with the author ready to integrate the DynamoDB data with Microsoft Dataverse.

In Summary

In conclusion, this blog post provided a comprehensive exploration into integrating AWS data with Microsoft Dataverse via Virtual Tables. The author concluded that the incorporation of external data sources can be easily achieved using an OData Endpoint. Future endeavours promise advancements in the model with the addition of another entity to the EDM model, the detailing of relationships between entities in EDM, and the completion of functionality for the OData endpoint to facilitate full CRUD operations.

Read the full article AWS Data in Dataverse with Virtual Table – Part 1

Microsoft Dataverse - Part 1: Integrating AWS Data into Dataverse via Virtual Table

Learn about AWS Data in Dataverse with Virtual Table – Part 1

The following text is a guide on how to seamlessly integrate data from AWS into Microsoft Dataverse using the Virtual Tables. It provides an in-depth analysis and steps on how to make this integration possible. Keywords in this text include Microsoft Dataverse, Data integration, AWS DynamoDB, Business Central, OData Endpoint and Virtual Tables.

If you are interested in learning how to implement this functionality, there are several training courses you might consider. Microsoft provides available courses on Azure data solutions, APIs, and data integration, which are vital in this context. Look out for courses such as 'Microsoft API and data integration with Power Platform,' or 'Data solutions in Microsoft Azure.'

Moreover, Amazon also has several training resources under AWS trainings which could aid in understanding the functionality of DynamoDB and cloud solutions. A combination of these two will give you a balanced knowledge for this integration.

After acquiring this training, you will be able to understand data sources and their importance, determine where data exists and how to access it using connectors, determine the use of virtual tables, and understand how to navigate within the Microsoft UI. This will allow you to integrate and access business data from Business Central in Dataverse irrespective of the storage medium.

With these skills, you can even set up a data source for any cloud database like DynamoDB. You will learn how to create an OData v4 Data Provider and specify Query Parameters and Header Parameters for your endpoint. Generally, this will open up a world of data accessibility straight from your Dataverse.

On the AWS side, you will gain skills on how to create an OData Endpoint in AWS. AWS DynamoDB coupled with AWS Serverless Application will set the stage for a seamless data fetch from AWS to Dataverse. This might be escalated to API Gateways on AWS, thus leading to a range of data operations including Create, Read, Update, and Delete (CRUD).

The journey does not end at acquiring skills and knowledge. As an individual or an institution, make an effort to apply the learned knowledge. Set up the Microsoft Dataverse and try to make a data integration from AWS. Test the API endpoints with Postman or any other API testing tool. This will validate your progress and probably create more curiosity for more knowledge.

So, let's be brave and curious! Join the new journey and see how to integrate your external data as virtual tables in Dataverse. With accessible courses, detailed guides, and active practice, this is achievable. The benefit of being able to access and control your data from various sources will have a huge positive impact on time management and decision making, making it worth your while.

Create a platform and experiment, make mistakes and learn from them. This way, you will have no questions, as every scenario will be covered practically. It's only through practice that we perfect our skills.

  • Develop curiosity for uncovering new skills and techniques

  • Source appropriate Microsoft and AWS training courses to enhance your skills

  • Practical application of knowledge and skills is critical for understanding

More links on about AWS Data in Dataverse with Virtual Table – Part 1

AWS Data in Dataverse with Virtual Table - Part 1
Jan 29, 2023 — Let's imagine you can use your Amazon AWS data in Microsoft Dataverse as virtual tables. This opens up a world of new possibilities...
AWS Data in Dataverse with Virtual Table - Part 1
Let's imagine you can use your #Amazon #AWS data in #Microsoft #Dataverse as virtual tables. Yes, this opens up a world of new possibilities!
AWS Data in Dataverse with Virtual Table - Part 1
Let's imagine you can use your #Amazon #AWS data in #Microsoft #Dataverse as virtual tables. Yes, this opens up a world of new possibilities!


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