21 docs tagged with "Data & Analytics"

The purpose of Data Products in the context of SAP is the efficient and standardized sharing and consumption of data across applications and domains. It helps ensure high-quality metadata, is optimized for intensive reads, and describes the lineage and interfaces available for integration.

SAP Datasphere's data integration architectures that harmonizes SAP and non-SAP data using data fabric architecture helps business experts to make data-driven decisions with unprecedented impact.

The SAP Federated Machine Learning Python library (FedML) applies the data federation architecture of SAP Datasphere for intelligently sourcing SAP and non-SAP data for Machine Learning experiments, run on any Machine Learning platform, thereby removing the need for replication or data movement. By abstracting data connection, data discovery, data loading (for all ML platforms), model training, model deployment, and inferencing (for hyperscaler machine learning platforms), the FedML library offers end-to-end integration with just a few lines of code.

FedML's IBM watsonx support helps data scientists accelerate machine learning workflows with IBM watsonx workflows, while providing instant access to SAP's critical business data thereby eliminating the need to duplicate data for model training.

FedML-AWS provides end-to-end integraton for training models in Amazon Sagemaker using live business data from SAP systems and eliminates the need for duplicating the data.

FedML-Azure provides end-to-end integraton for training models in Azure Machine Learning service, using live business data from SAP systems and eliminates the need for duplicating the data.

FedML-Databricks provides end-to-end integraton for training models in Databricks machine learning platform, using live business data from SAP systems and eliminates the need for duplicating the data.

FedML-GCP provides end-to-end integraton for training models in Google Vertex AI using live business data from SAP systems and eliminates the need for duplicating the data.

FedML now supports reading of federated SAP business data via SAP Datasphere directly into NVIDIA GPU environment computes for model training.

Insight Apps are delivered in SAP Business Data Cloud as a prebuilt set of artifacts, from Data Products, to models, to stories in SAP Analytics Cloud. They allow you to configure your entire environment simply by subscribing.

Data from AWS data sources can be harmonized with SAP and non-sap data via SAP Datasphere's data fabric architecture.

Data from Microsoft Fabric analytics platform 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.

Data from Databricks Lakehouse can be harmonized with SAP and non-sap data via SAP Datasphere's unified data models for use with richer analytics and other use cases.

Data from Google Cloud Platform (GCP) data services can be seamlessly integrated and harmonized with both SAP and non-SAP data using SAP Datasphere's advanced data fabric architecture.

In data federation scenarios, particularly where non-SAP and SAP data are virtually queried to avoid duplication, performance and latency are critical factors that influence the effectiveness of the data federation architecture for analytics solutions

This reference architecture demonstrates a common use case for integrating various types of data—structured, semi-structured, and unstructured—into SAP HANA Cloud, utilizing the powerful features of SAP HANA Cloud Data Lake. By implementing this architecture, businesses can meet the challenge of providing a modern data foundation for intelligent data applications and provide cost-effective data management and analytics across the enterprise.

Modernize SAP Business Warehouse (BW) with SAP Business Data Cloud (BDC) to unlock real-time analytics, AI-driven insights, and scalable cloud-native architecture. Leverage SAP Datasphere, SAP Analytics Cloud, and data products to transition seamlessly while preserving existing investments. Discover structured migration pathways, advanced AI/ML capabilities, and unified data management for future-ready enterprise data strategies.

SAP and Databricks have partnered to integrate SAP data with Databricks AI and analytics platform, allowing businesses to leverage SAP data for AI and machine learning applications. This partnership simplifies data access and eliminates the need for complex ETL processes, enabling real-time analytics and AI-driven decision-making.

The reference architecture for Esri running on SAP HANA Cloud as a geodatabase represents a powerful integration of geospatial technology with enterprise-grade cloud infrastructure. This enhances geospatial capabilities by allowing organizations to store, process, and analyze spatial data directly within SAP HANA Cloud, utilizing its multimodel processing and built-in spatial engine. The integration provides real-time access to both SAP and non-SAP data, breaking down data silos and enabling near-instant insights, which is crucial for industries like utilities during natural disasters.

Discover SAP BDC and S/4HANA integration with SAP-managed Data Products and Insight Apps for advanced analytics. Learn how to produce, activate, and visualize data products using SAP Datasphere and SAP Analytics Cloud. Explore pre-built Insight Apps for actionable intelligence across Core Enterprise, People, Spend, Customer, and Supply Chain Analytics. Maximize business insights with SAP's trusted data foundation and lifecycle management. Optimize decision-making with SAP's seamless architecture.

SAP Business Data Cloud (BDC) unifies SAP and non-SAP data, enabling advanced analytics, governance, and AI-driven insights. With tools like SAP Datasphere, SAP Analytics Cloud, and Databricks integration, SAP BDC addresses data silos, improves data quality, and supports real-time processing. Modernize legacy systems, create reusable data products, and leverage a unified semantic model for scalable, future-ready enterprise data strategies.