Connected applications using Power Platform Common Data Service and Model (CDS/CDM)
Simplified data management and application development with Common data service and common data model
Microsoft Dynamics and PowerApp based applications have been using Common Data Service (CDS). Users of Dynamics and PowerApp are familiar with CDS. Microsoft has started extending the features in other parts of the Azure ecosystem. For Power BI users, CDS also becomes a part of the Big Data solution. CDS helps in building Power BI solutions for large data sets.
What is Common Data Service
CDS is like a data store that allows structuring variety of data, and business processes and rules to build connected applications. To provide an analogy, CDS is similar to a database. The entities inside CDS are like tables in a database. Fields are like columns, and relationships are like keys. Views are like stored select statements similar to database views. CDS is available to store data for applications build on PowerApp and Dynamics without deploying an external SQL database.
CDS comes with rich pre-defined entities and data models. New entities can be added to build, and existing entities can be enhanced to support custom applications. CDS become inevitable in business process automation. Organizations can create PowerApp applications, store data in common data service and digitize the manual processes.
Power BI Reports
Power BI can directly connect to common data service using the Power Platform connector. Reports and Dashboards can be built using Power BI for CDS coming through Dynamics or PowerApp. Through our Power BI consulting services, the data wrangling and data mashups efforts can be greatly reduced.
What is the Common Data Model
Common Data Model is an open-source initiative backed by Microsoft, SAP, and Adobe to create a standard industry data model, entities and attributes. The initiative standardizes data for the common business scenarios in most entities. The CDM defines a standard way of creating, organizing and storing the data. The format and structure are standardized in CDM which allows easy extensibility.
Azure Extensibility and connected data
Underline CDS data is store in Data Lake Gen 2 storage using the common data model. While CDS allows building PowerApp, Dynamics based applications and Power BI reports, it can largely be extended using CDM in the data lake. The data from CDM can be accessed directly in other external Azure services like Databricks, machine learning, search, cognitive services, etc.
We can also use other applications to store data in ADLS in CDM format which can be accessed through Power App or Power Platform. Using the common data model .json file external applications can describe the data format, data type, relationships, and partitions of the data being stored in ADLS CDM folders. Since the data is available in the standard format, it can be accessed through many Azure applications.
DataFlows
Power BI also leverages the CDM and extends the Power BI capabilities using Dataflows. Dataflows will prepare the data sets using standard Power Query features and stores them in CDM. Once the data is available in CDM, it can be accessed through any other Azure services.
Power BI Premium customers can use their own Data Lake Store to store the dataflow data in CDM which can be accessed and extended through other applications. Power BI dataflow also allows attaching a CDM folder created outside Power BI to be accessed through Power BI. It means we can access in Big data stored in Data lake Store (common data model format), directly in Power BI.
Dataflows benefits
- Power Query on the cloud in the browser. Don’t need a desktop application
- Use datasets in multiple reports
- Extend report data by bringing additional data sources in the same report
- Dataflow data accessed through Data Lake
- Manage data refresh scheduling
- Data snapshot for versioning
Microsoft is focusing on consolidating and standardizing the Azure cloud services. Using the CDS/CDM data, we can remove data silos and create connected applications. It centralizes the data in ADLS and avoids redundancy and multiple versions of the truth. Finally, the development can be greatly accelerated using pre-defined structures and entities.
This article was originally published on LinkedIn Pulse by our Data and AI Practice Lead, Tarun Agarwal.
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