Virtualize Workload - Datasphere an enabler - use cases

Concept of Virtualizing Workloads

Virtualizing workloads refers to the ability to access, process, and analyze data from multiple disparate systems in real time without the need for physically transferring or replicating the data. Instead, it creates a virtual layer to interact with data from its source systems. This reduces latency, minimizes storage costs, and enables real-time decision-making.

SAP Datasphere, an evolution of SAP Data Warehouse Cloud, is a key enabler of workload virtualization. It provides a unified platform that allows organizations to integrate, manage, and access data across SAP and non-SAP systems. Its semantic data layer ensures consistent data representation, regardless of the data’s physical location.

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How SAP Datasphere Enables Workload Virtualization

1. Federated Query Processing

SAP Datasphere uses federated queries to access data directly from source systems (e.g., SAP S/4HANA, SAP BW, non-SAP databases). This eliminates the need for extensive ETL (Extract, Transform, Load) processes, ensuring near real-time data access.

2. Data Orchestration

It orchestrates data from various systems, enabling seamless integration between SAP and non-SAP data sources like Snowflake, BigQuery, or AWS.

3. Business Data Fabric

The Business Data Fabric layer ensures semantic understanding of the data, enabling business users to work with meaningful information rather than raw datasets.

4. Security and Governance

SAP Datasphere integrates with SAP BTP's security features, providing robust data access controls while ensuring compliance.

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Example: Virtualized Workloads in the Automobile Industry

Use Case 1: Real-Time Inventory Management

An automobile manufacturer with plants worldwide needs real-time visibility into inventory levels across locations.

Challenge: Data resides in SAP S/4HANA, legacy ERP systems, and a cloud-based supplier portal.

Solution:

SAP Datasphere connects all data sources via its virtualized layer.

Real-time dashboards show inventory levels, enabling just-in-time production scheduling.

Use Case 2: Predictive Maintenance

A car company wants to analyze IoT sensor data from vehicles to predict maintenance requirements.

Challenge: Sensor data is stored in AWS, while customer and vehicle history is in SAP S/4HANA.

Solution:

SAP Datasphere integrates IoT data and SAP customer records without replication.

Predictive analytics models run on combined datasets, ensuring timely alerts for maintenance.

Use Case 3: Dealer Sales Performance

The company needs to analyze sales performance data from global dealers.

Challenge: Dealer sales data is in an external CRM, while financial data is in SAP systems.

Solution:

SAP Datasphere provides a unified view by virtualizing CRM and SAP data.

Insights into dealer performance help optimize sales strategies.

By virtualizing workloads with SAP Datasphere, the automobile industry can reduce complexity, improve real-time decision-making, and enhance operational efficiency without massive data duplication efforts.