Develop Applications and Integrate Data with a Cloud Database

Integrate data from across your enterprise using the cloud-based data foundation for SAP Business Technology Platform

What is SAP HANA Cloud?

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What is SAP HANA Cloud?

SAP HANA Cloud is a fully managed Database as a Service (DBaaS). This builds upon the established capabilities of the SAP HANA Platform.

SAP HANA Cloud can integrate data from any source and in any form, as it is a platform for modern data and analytics applications. This can scale to meet your growing demands and provide deep integration with your on-premise and cloud systems.

SAP HANA Cloud will accelerate access to data through its real-time capabilities and produce deeper insights through the application of machine learning techniques. The resultant data can be consumed through an analytics tool of your choice.

Why Do Organisations Need SAP HANA Cloud?

Connect to all your data assets wherever they may be within your enterprise – on-premise or cloud, structured and unstructured
The required data can be virtually accessed in SAP HANA Cloud or alternatively, materialised in SAP HANA Cloud
Deliver new and relevant insights across datasets that are not currently being stored or processed
Apply Machine Learning algorithms by using the inbuilt capabilities
Provide real-time, impactful business insights
Improve the accuracy and trustworthiness of data through using the rich data cleansing capabilities
Secure data at source and provide the relevant data to different users and parts of the organisation – irrespective of client application
Designed to be the best at working with SAP applications and data
A simple cloud-based solution for all your data requirements

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Thanks to SAP's hybrid analytics architecture and Cloud Conversion Credit scheme, you can extend your BusinessObjects landscape with new self-service, planning and predictive capabilities built on your existing investments in Universes and Web Intelligence.

SAP Analytics Cloud for Planning Rapid Adoption Package

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Connectivity: Connect to diverse systems and data sources using the Data Provisioning Application (DPA) as a secure mechanism. The DPA supports a wide range of adapters to provide simplified access to external data sources.
Virtualisation: Data can be virtually established within SAP HANA Cloud to eliminate the need for data duplication and ensure that the data is always current. This can be adapted at any stage to physically store the data in SAP HANA Cloud. This may be required to address performance issues or to apply advanced data processing algorithms to the data
Multi-tiering: Data can be held in many different forms and this will affect the cost and performance of this data. In addition to virtual data, data can be stored in memory to maximise its responsiveness. Lower priority data can be stored on disk to reduce memory costs and finally, data can also be acquired by the accompanying Data Lake. The Data Lake provides a scalable and economical means for storing data
Performance: The use of column storage and data compression, coupled with the in-memory capabilities will provide high-speed access to data and remove any constraints that may have existed at source
Elasticity: Systems can be scaled up or down by altering the computing and storage resources. This allows organisations to flexibly meet their demands and ensure that the end-user experience is not impacted by an oversubscribed system
Spatial: Spatial data can be stored in its native form to support geospatial querying. This may include the calculation of distances, intersections and other similar queries
Graph: The Graph engine can model different types of networks and linked data. Relational data storage supports full use of the Graph engine and can be leveraged to determine relationships between data nodes and traverse the paths connecting these nodes.
Machine Learning: The inbuilt PAL and APL algorithms can be applied to datasets to produce rich insights. There are defined approaches to train, apply and maintain models to produce the optimal outcomes
Text Search: A powerful search engine can provide a search facility across tables and documents. This uses literal and fuzzy techniques to find relevant matches. The text engines can also decipher sentiment and extract entities from textual data
Data Privacy: The security subsystem allows users to define the appropriate level of security across different datasets and users. Additionally, data can be anonymised and masked to provide the desired level of data confidentiality