Skip to main content

Azure data integration with SAP Datasphere

Last updated on by s-krishnamoorthy

This content has been archived

This content has been archived. It describes a retired or obsolete approach and is no longer maintained or supported by SAP. It remains available for reference only and should not be relied on as current guidance.

Joint Reference Architecture

This content was developed in collaboration with Microsoft.

Microsoft Fabric is an end-to-end, unified analytics platform that integrates technologies like Azure Data Factory, Azure Synapse Analytics and Power BI into a single unified product.

Data from the Microsoft Fabric Analytics platform - OneLake (the foundational component of Fabric, serving as a single source of truth for analytics data) can be seamlessly integrated and harmonized with both SAP and non-SAP data using SAP Datasphere's robust data fabric architecture. This architecture facilitates the unification of disparate data sources, enabling comprehensive data modeling and analytics.

SAP Datasphere's data fabric architecture supports various modes of data integration, including data federation and data replication. Data federation allows real-time access to data without the need for physical data movement, ensuring up-to-date insights and reducing data latency. On the other hand, data replication involves copying data from source systems to SAP Datasphere, enabling high-performance data processing and analytics.

By leveraging SAP Datasphere, organizations can create a unified semantic layer that combines data from Microsoft Fabric's data platform with SAP business data. This unified view empowers users to perform advanced analytics and generate actionable insights using tools like SAP Analytics Cloud.

Furthermore, SAP Datasphere provides robust data governance and security features, ensuring that integrated data complies with organizational policies and regulatory requirements. This comprehensive approach to data integration and harmonization helps organizations maximize the value of their data assets and drive informed decision-making.

Architecture​

image of solution diagram
Copy to clipboard
Solution Diagram Resources
You can download the Solution Diagram as a .drawio file for offline use. Alternatively, you may view and edit the Solution Diagram directly on draw.io.
Please note that any changes made online will need to be saved locally if you wish to keep them.
DownloadEdit

1. Integration with Fabric Synapse Real-Time Analytics​

Mode(s) of Integration: Federating data live into SAP Datasphere.

Fabric Synapse Real-Time Analytics is a fully managed, cloud-scale data analytics solution built on the Kusto engine with Azure Synapse. It is designed for real-time analysis of large volumes of data streaming from applications, websites, IoT devices, and more. It provides a powerful query engine and a highly optimized data storage architecture, making it ideal for interactive analytics and complex data exploration. Synapse Real-time Analytics fully encompasses capabilities of the legacy Azure Data Explorer in Microsoft Fabric unified platform.

Federating Data from Fabric Synapse Real-Time Analytics into SAP Datasphere​

Data from Fabric Synapse Real Time Analytics can be federated live into SAP Datasphere remote models using SAP Datasphere's data federation architecture. This approach allows for real-time access to data without the need for physical data movement, ensuring that the most current data is available for analysis.

Technical Details and Examples​

  1. Setting Up the Connection:

    • Prerequisites: Ensure you have a Synapse workspace with Real-Time Analytics enabled, necessary credentials with proper access to the Synapse workspace, and SAP Datasphere access with privileges to create remote connections and models.
    • Connection Configuration: Use the SAP Datasphere connection management interface to configure a new connection to Fabric Synapse Real-Time Analytics. Provide the required connection details such as the endpoint URL, database name, and authentication credentials.

  2. Creating Remote Tables:

    • Remote Table Definition: Define remote tables in SAP Datasphere that map to the tables or queries in Fabric Synapse Real-Time Analytics. This can be done using the remote table creation wizard in SAP Datasphere. These remote tables point to Kusto tables or KQL (Kusto Query Language) queries in Synapse Real-Time Analytics.
    • Example Query: 
      .create table MyTable (Timestamp: datetime, DeviceId: string, Temperature: real)
    • Federation Query: Use SAP Datasphere to create a remote table that references the above table in Fabric Synapse Real-Time Analytics.

  3. Data Augmentation:

    • Combining Data: Augment the federated data from Fabric Synapse Real-Time Analytics with SAP business data. For example, you can join sales data from SAP S/4HANA with IoT sensor data from Fabric Synapse Real-Time Analytics to analyze the impact of environmental conditions on product sales.
    • Example Join Query: 
      SELECT 
         SalesData.ProductID, 
         SalesData.SalesAmount, 
         SensorData.Temperature 
      FROM 
         SalesData 
      JOIN 
         SensorData 
      ON 
         SalesData.DeviceId = SensorData.DeviceId

  4. Real-Time Analytics:

    • Dashboards and Reports: Use SAP Analytics Cloud to create dashboards and reports that visualize the federated data. This enables real-time monitoring and analysis of key metrics.
    • Example Dashboard: Create a dashboard that shows real-time temperature readings from IoT devices alongside sales performance metrics.

2. Integration with MS Fabric OneLake​

Mode(s) of Integration: Replicating data out with Replication Flows, Importing data into SAP Datasphere using Data Flows.

OneLake is a single, unified data lake that serves as a centralized repository for all organizational data. It's built on top of Azure Data Lake Storage (ADLS) Gen2 which is a scalable and secure data lake solution designed to handle large volumes of data in various formats. It supports big data analytics and is optimized for high-performance workloads.

Importing Data into SAP Datasphere​

Non-SAP data from Azure Data Lake Storage can be imported into SAP Datasphere using the Data Flow feature. This allows organizations to leverage data stored in Azure Data Lake for applications such as financial planning and business analytics in SAP Analytics Cloud.

Steps to Import Data:​

  1. Set Up Data Flow:

    • Prerequisites: Ensure you have the necessary permissions and credentials to access both Azure Data Lake Storage and SAP Datasphere.
    • Configuration: Use the SAP Datasphere interface to create a new data flow. Provide the required connection details such as the storage account name, container name, and authentication credentials.

  2. Define Data Transformation:

    • Data Mapping: Map the data fields from Azure Data Lake Storage to the corresponding fields in SAP Datasphere.
    • Transformation Rules: Apply any necessary data transformation rules to ensure the data is in the correct format for analysis.

  3. Load Data:

    • Execution: Execute the data flow to import the data into SAP Datasphere.
    • Validation: Validate the imported data to ensure accuracy and completeness.

Replicating Data to OneLake​

Data from SAP source systems such as S/4HANA and BW/4HANA can be replicated to Microsoft Fabric's OneLake data platform by replicating it to Azure Data Lake Storage Gen2 using SAP Datasphere's Replication Flows. This enables organizations to store and analyze SAP data alongside other enterprise data in a unified data lake.

Steps to Replicate Data:​

  1. Set Up Replication Flow:

    • Prerequisites: Ensure you have the necessary permissions and credentials to access both SAP source systems and Azure Data Lake Storage.
    • Configuration: Use the SAP Datasphere interface to create a new replication flow. Provide the required connection details such as the SAP system credentials and Azure Data Lake Storage account information.

  2. Select Data for Replication:

    • Data Selection: Choose the specific tables or datasets from the SAP source systems that need to be replicated.
    • Scheduling: Schedule the replication flow to run at desired intervals to ensure data is kept up-to-date.

  3. Monitor Replication:

    • Execution: Execute the replication flow to start the data replication process.
    • Monitoring: Monitor the replication process to ensure it completes successfully and troubleshoot any issues that arise.

  4. Access the replicated data in OneLake: -Data replicated into Azure Data Lake Storage Gen2 can be accessed virtually in OneLake via shortcuts or can be moved into OneLake via the Data Flow workflows.

For detailed step by step information and to try out the integration, visit the github : Integrate MS Fabric with SAP Datasphere