Getting Started with SAP Datasphere


After completing this lesson, you will be able to:

  • Provide an Overview of SAP Datasphere

SAP Datasphere Overview

SAP Datasphere enables a business data fabric architecture that uniquely harmonizes mission-critical data throughout the organization, unleashing business experts to make the most impactful decisions. It combines previously discrete capabilities into a unified service for data integration, cataloging, semantic modeling and data-warehousing. It also virtualizes workloads spanning SAP and non-SAP data. SAP Datasphere preserves the full meaning and context of SAP data across systems and clouds. It integrates with other data vendor’s platforms, delivering seamless and scalable access to one authoritative source for your most valuable enterprise data. SAP Datasphere leverages existing data investments. It does not require moving data into yet another data store. It radically simplifies your data landscape, ensuring inherent governance throughout the data life-cycle.

From March 8th, 2023, SAP presents the successor product for the SAP Data Warehouse Cloud, SAP Datasphere. You can continue using existing implementations in the SAP DWC as usual. Investment security is guaranteed. Because SAP manages the software update, the customer need not do anything. SAP Datasphere is characterized by the stronger, functional integration of SAP Data Intelligence, as well as the new Analytic Model as the central data model type. In addition, the data catalog will expand so that a central metadata repository is made available.

In addition, SAP has announced strategic partnerships with leading data and AI companies including Collibra, Confluent, Databricks, and DataRobot. These partnerships expand SAP Datasphere and enable companies to build a unified data architecture that securely brings SAP software and third-party data together.

The intention for Collibra is to integrate it with SAP through a customized integration. This enables customers to implement a strategy for enterprise-wide data management, building a complete data catalog with information about the data's origin across their entire data landscape, including both SAP data and third-party data. Collibra ensures that every company can find and utilize trustworthy data.

Confluent plans to integrate its data streaming platform with SAP, enabling companies to unlock valuable business data and connect it to external applications in real time. Confluent's cloud offering is the central platform for continuous real-time data streams from various sources in an organization.

Databricks enables its customers to integrate their data lake into SAP software, enabling data exchange and preserving semantics. This helps them simplify their data landscape.

DataRobot enables customers to utilize automated machine learning capabilities for multimodal data on SAP Datasphere. It also enables them to integrate it directly into their data fabric for business data management, regardless of the cloud platform they are on.

SAP Datasphere provides a multi-cloud, multisource business semantic service for enterprise analytics and planning. SAP Datasphere is the latest innovation in the data warehousing portfolio of SAP. It is based on the SAP HANA Cloud and follows a clear data warehouse as a service (DWaaS) approach in the public cloud, with fast release cycles.

The figure, SAP Datasphere Architecture, shows a high-level architecture of SAP Datasphere.

Business modeling (self service): Use graphical low-code or no-code tools that support self-service modeling needs for business users, multi-dimensional modeling with powerful analytical capabilities, and a built-in data preview.

Data modeling: Use graphical low-code or no-code tools, powerful built-in SQL, and data-flow editors for modeling, transformation, and replication needs. Enrich existing datasets with external data, coming from the Data Marketplace, CSV uploads, and third-party sources.

Data Marketplace: Data Marketplace is fully integrated into SAP Datasphere. It is tailored for businesses to easily integrate third-party data. You can search and purchase analytical data from data providers. The data comes in the form of objects packaged as data products, which you can use in spaces of your SAP Datasphere tenant. Data products are either provided for free or require the purchase of a license at a certain cost. Some data products are available as one-time shipments. Data providers regularly update other data products.

Data space: To provide secure modeling environments for different departments and use cases, centrally create and provision spaces . Allocate disk and in-memory storage to spaces, set their priority, add users, and use monitoring and logging tools to manage spaces.

Data catalog: A catalog is a comprehensive solution that collects and organizes data and metadata, enabling business and technical users to make confident data-driven decisions. Catalog improves productivity and efficiency by building trust in enterprise metadata through consistent data quality and governance.

Data quality (governance): Publish high-quality trusted data and analytic assets, glossary terms, and key performance indicators to a catalog. This supports self-service discovery and promotes their reuse.

Data orchestration: SAP Datasphere can connect to SAP, non-SAP cloud, and on-premise sources, including data lakes, to federate, replicate, and transform and load data. Re-use and migrate trusted meta and data models residing in SAP Business Warehouse and SAP SQL Data Warehouse implementations.

The overall SAP Datasphere architecture consists of the following components:

  • Space management: Create spaces tailored to your business needs and monitor them. You model your data within spaces. Spaces are decoupled, yet open for flexible access. Decide on the size, storage type, and importance of your spaces. Add users and connect your sources in the spaces.
  • Data integration and data flow: Access data from SAP and non-SAP on-premise and cloud sources. Connect data virtually or replicate it in real time or scheduled via data flows powered by SAP Data Intelligence. Use APIs or tools to access data. Implement data preparation with transformations and scripting for advanced requirements.
  • Data builder: Define data models for your data using a technical, model-driven approach in the graphical tools or in the powerful SQL editor of the data layer. As a data engineer, model, combine, and harmonize data in a unified, standardized way. Flexibly update data models.
  • Business builder: Model your business scenarios in the graphical tools of the business layer independently of the data layer. As a BI analyst, model in a demand-driven way using business language to answer common questions independently of IT. Map your models to the data layer.
  • Administration and security: Manage settings on system level, such as connectivity and data integration settings, security, auditing, and monitoring settings. Manage self-service IP allowlists, database access, and others. Define security on all layers. Manage access on tenant, functional, and space level, and configure secure connectivity to sources. Manage data access by defining row-level security on data and business layer. Enable auditing for read and change operations.
  • Semantic Onboarding: The Semantic Onboarding app provides a central entry point to import semantically-rich objects from your SAP systems and the Content Network, as well as the Public Data Marketplace and other marketplace contexts.
  • Consumption: Connect your standalone SAP Analytics Cloud solution or consume available models with your SQL tool or BI clients of choice.

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