SAP Datasphere, previously known as SAP Data Warehouse Cloud, represents a significant evolution in data management and analytics solutions offered by SAP. At its core, the technology runs on an SAP HANA Cloud database, offering clients new ways to model and manage data compared to legacy data management solutions. These new ways to model data can significantly increase the agility of data pipeline management during the initial build process and when maintaining models in production. SAP Datasphere achieves this by ingesting source system data at a base layer; further modeling is based on virtual models (like SQL views) that do not require further persisting or physical movement of data within the data pipeline. This method is often referred to as the extract, load, (then) transform (ELT) method. In addition to agility, this methodology also supports true real-time analytics for business consumers of the data. With that said, SAP Datasphere also supports the legacy extract, transform, load (ETL) method in a variety of traditional and creative new ways. This makes SAP Datasphere the ideal platform to manage data in the modern cloud computing world.
Core features
- Data integration: SAP Datasphere supports robust data integration capabilities, allowing organizations to integrate data from various sources seamlessly. It strongly supports SAP various sources but also support many modern non-SAP data sources, ensuring a unified view across the enterprise.
- Robust incremental data loads: SAP Datasphere supports batch loading of data from all sources. In addition, many of these same sources also provide true change data capture (CDC) delta loading or real-time federation from the source.
- Data cataloging: The platform offers advanced data cataloging features that help organizations manage their data assets efficiently. It allows for easy discovery, governance, and semantic modeling of data.
- Semantic modeling: Semantic layers in SAP Datasphere enable users to create models that reflect business terminology and logic. This feature simplifies complex data structures into understandable business terms, facilitating easier analysis and reporting.
Technical capabilities
- Open data ecosystem: The system supports an open data ecosystem approach, which is fundamental for creating a business data fabric architecture. This feature ensures that data can be easily accessed and shared across different business units within an organization, maintaining consistency and integrity. This includes consumption of SAP Datasphere data with non-SAP visualization tools and modern data lake technologies.
- AI integration: Integrating AI platforms is straightforward with SAP Datasphere. It helps deliver contextually relevant insights by leveraging AI to analyze extensive datasets from multiple systems and locations.
- Governance and security: The platform provides robust governance tools that help ensure compliance with internal policies and external regulations. Additionally, it offers comprehensive security features to protect sensitive information against unauthorized access.
- Virtual modeling: Virtual modeling means that data is not transformed and copied into redundant tables; the model output is not physically stored like we often see in other ETL based solutions and many modern cloud-based data lake technologies. This, coupled with columnar data storage and in-memory storage, make SAP Datasphere the ultimate ELT platform. ELT solutions facilitate real-time access to KPIs and results in an architecture that supports increased agile development. Agility in the development process results in faster enhancements and break/fixes and allows IT to respond much faster to data consumers demand.
Deployment options
- Users have flexibility in deployment options including cloud-based services facilitated by SAP’s robust infrastructure which guarantees reliability and scalability. SAP Datasphere is supported running on Google, AWS and Azure cloud platforms.
Practical usage
- Customization: Users can customize technical names and business names of objects within the system. The “import and deploy” functionality allows for remote tables to be generated with semantic usage as relational dataset. In addition, custom business logic can be applied at each layer of modeling to present consumers with data, how they expect or need it to appear.
- Data management utilities: SAP Datasphere offers robust lineage tracing, data profiling and data governance tools to help both IT and business users understand their data pipeline, transformation and data quality.
- Modeling with SQL: SAP Datasphere modeling, both graphical and scripted is based on traditional SAP HANA database SQL and SQLScript resulting in easier adoption by experienced.
- Easy administration: Most features and capabilities within SAP Datasphere are carried out within the graphical user interface without the need to execute code, change configuration files or perform complex configurations.
Four areas of focus
Based on our experience, customers implementing SAP Datasphere often face several challenges. It’s important to understand these challenges when architecting SAP Datasphere. We often find organizations struggle with these five areas of focus when implementing SAP Datasphere.
- Performance optimization: Handling large volumes of intricate data analysis can sometimes lead to less-than-ideal performance times for end-users. Therefore, designing models and data pipelines that enhance SAP Datasphere’s performance becomes a crucial task.
- Understanding SAP Datasphere: Before implementing this solution, it’s essential to comprehend the concept of SAP Datasphere and its role in resolving typical data management issues. This understanding can be a challenge for some organizations that are experienced in traditional ETL (batch-based) solutions like SAP BW.
- Integration and data management: SAP Datasphere provides a robust platform for integrating multiple data sources and performing complex analysis; however, managing these different sources and ensuring their seamless integration can pose a significant challenge. How to structure and organize objects in SAP Datasphere spaces, folders and how to approach modeling the data must be carefully planned and executed.
- Memory management: In certain circumstances, processes like ‘view persistency’ or complex virtual modeling may consume too much memory or cause out-of-memory exceptions, posing difficulties in handling and optimizing resource use within the system.
SAP Datasphere Migration Accelerator
Before organizations implement SAP Datasphere, we highly recommend they conduct a detailed assessment of their landscape and properly plan the transition from existing SAP BI tools and platforms. At Protiviti, we have developed an SAP Datasphere Migration Accelerator to help clients efficiently navigate the transition and subsequently execute a detailed migration plan. Our assessment provides:
- Knowledge sharing workshops: Empower technical and business decision-makers with knowledge of the full capabilities, architecture and components of the SAP Datasphere. Fundamental knowledge-sharing workshops will increase the efficiency of key players through all phases of the project planning.
- Data landscape assessment: Collect relevant technical and business information required to properly design a migration strategy. Catalog current state data pipelines. These documented assessment results are leveraged for the project planning phase, future state architecture, SAP software cost estimations and overall strategy.
- Future state requirements and goals: Define long-term SAP data management goals to design the ideal SAP Datasphere architecture. Capture overall change impacts, total cost of ownership (TCO) goals and future-state analytics capabilities.
- Migration strategy planning: Analyze the technical migration process to establish an efficient project, resource and migration plan. We produce documents that can directly be used to create a project plan, cost estimate and migration strategy.
- Organizational change management: Incorporate a sound OCM strategy to increase awareness, adoption and knowledge across the organization for a seamless migration and transition.
Protiviti’s SAP practice is well-equipped to help clients plan, execute and maintain a robust SAP Datasphere environment. In addition to our SAP Datasphere Migration Accelerator, we also offer clients a robust base layer of prebuilt S4/HANA CDS views and SAP Datasphere facts and dimensions that are designed to accelerate any implementation. Our prebuilt content can save thousands of implementation dollars and has been fully validated. In addition, all content is fully delta change data capture optimized in support of the ideal ELT methodology enabling clients to fully leverage the advantages of in-memory cloud-based computing.
To learn more about our SAP consulting services, contact us.
