introduction:

In my previous blog we explored how organizations increasingly leverage hyperscaler platforms like Amazon Web Services, Google Cloud Platform, and Microsoft Azure for cost-effective and scalable data management, the need to seamlessly integrate and analyze data from these hyperscale environments with SAP’s business applications has emerged as a critical business requirement.

Today let’s explore how the combination of Google Cloud and SAP Datasphere brings forth a seamless blend of analytical prowess and data processing capabilities.

In the current business landscape, analytical inquiries often require combining of data from diverse sources. For organizations seeking to run machine learning experiments on hyperscaler platforms using their SAP business data, the traditional approach of replicating data introduces challenges such as data duplication, loss of data semantics, and a lack of real-time access.

By leveraging Datasphere federation capabilities, organizations can seamlessly combine SAP business data with information stored in popular hyperscaler or SaaS offerings. The outcome is a comprehensive solution that provides real-time access to SAP S/4HANA data, robust security and governance features, and self-service analytical capabilities through SAP Analytics Cloud.

Google & SAP Datasphere Integration: A Powerful Alliance

The solution diagram paints a picture of collaboration, showcasing the convergence of SAP Business Technology Platform, SAP Datasphere, and Google Cloud services like BigQuery, Google Cloud Storage, and Vertex AI. SAP Datasphere, with its business data fabric architecture, harmonizes critical data, empowering organizations to make impactful decisions. Check this blog here for More on Datasphere Explore your Hyperscale Data with SAP Datasphere

• Federating Data from Google BigQuery

Google BigQuery steps into the spotlight as an analytical data store, especially for external data like Google Analytics, Trends, and Ads data. By federating this data into SAP Datasphere, you tap into the in-memory capabilities of SAP HANA, enabling analysis of combined datasets from SAP and 3rd party sources in real-time. Say goodbye to lengthy data transfers and costly ETL processes.

• Importing Data into SAP Datasphere from Google Cloud Storage

Enter SAP Data Flow, the ETL capabilities of SAP Datasphere. With Data Flow, datasets from cost-efficient object stores like Google Cloud Storage can be seamlessly imported. Transform data during the transfer and persist it in SAP Datasphere – a smart move for efficient and effective data management.

• Using “Live” Data for Inference and Training with Google Vertex AI

Traditionally, using hyperscaler machine-learning or AI services requires data persistence on the hyperscaler platform. Here’s where the SAP FedML Library shines. Access data in SAP Datasphere, virtually or physically, and directly employ it in Jupyter Notebooks for ML model training or inference with Google Vertex AI. A game-changer in data accessibility!

Now , Let’s dive more into the Use Case: Unleashing the Power of Data Federation by accessing BigQuery data in SAP Datasphere without the need for replication.

Unlock the potential of data federation by accessing BigQuery data in SAP Datasphere without the need for replication. Create remote tables in SAP Datasphere for BigQuery Tables, combining them with data from other sources. This mission focuses on seamlessly accessing data residing in Google BigQuery from SAP Datasphere.

Current Challenge: Federating Data for Business Value

The challenge lies in federating data from Google BigQuery with SAP-held data to deliver significant business value. Integrating data from hyperscaler storage with critical data in SAP Datasphere is the goal – making data residing in different sources less relevant.

Destination: Enabling Richer Insight

The outcome is richer insight. By integrating SAP Datasphere with Google BigQuery services, businesses gain the ability to federate queries via virtual tables in SAP Datasphere without the need for data replication. Data stays in source systems, ensuring real-time connectivity and avoiding unnecessary caching or replication.

How You Get There: Configuring for Success

Configure SAP Datasphere by connecting to Google BigQuery following SAP best practices. Once configured, queried data from Google BigQuery becomes available in SAP Datasphere via virtual tables – no data replication needed. These virtual tables can be seamlessly used in views and procedures, establishing live connectivity between BigQuery data and SAP Datasphere.

In Conclusion: