Launched in New York last year, SAP Data Hub is a unique data solution that centralizes landscape orchestration and governance, meta data management, and lifecycle management. In SAP HANA Data Management Suite, SAP HANA forms the outer shell on which applications can be developed, guaranteeing that they can also be run in distributed environments to support big data scenarios. As a key feature of the new suite, the SAP HANA database platform also includes numerous built-in native analytical engines, including spatial processing, graph processing, text analysis, search and mining, machine learning, streaming, and series data storage and processing. With the in-memory performance required to handle today’s data volumes, SAP HANA is the business data platform for the intelligent enterprise. The suite is based on existing proven SAP technology that has demonstrated significant value to customers. Lifting the Hood on SAP HANA Data Management Suite With one query, SAP’s modern data platform can deliver results from a single logical data set that covers your whole enterprise – turning the concept of universal data into a reality. SAP HANA Data Management Suite is the foundation to build agile, data-driven applications that are always tapping into live data. It allows for secure, governed enterprise-class applications and analytics by providing an open, hybrid, and multi-cloud enabled solution suite that orchestrates all the data you need into a trusted, unified landscape. To meet these challenges, SAP has launched SAP HANA Data Management Suite, a complete end-to-end data solution based on SAP HANA, SAP Data Hub, and SAP Enterprise Information Management (EIM) technologies. Against this background, many companies have concerns about the security and accuracy of the data they use to make business-critical decisions every day. What is missing is the enterprise-ready technology to achieve these goals. They require flexibility when it comes to their cloud infrastructure and the architecture of their solutions. They want to be able to productize new data scenarios quickly and easily and gain a comprehensive source-agnostic overview of their business by combining all sets of data – whether big data, transactional data, or analytical data – into a single data universe. They need a solution that simplifies their landscape while also ensuring the highest levels of security. As data landscapes become increasingly complex, companies face the challenge of trying to manage the influx of both structured and unstructured data from multiple applications, files, databases, data warehouses, and data lakes. The Need for End-to-End Data Managementīut the introduction of new and advanced technologies can also bring new challenges. An end-to-end process that, for example, collects sensor data to provide new pay-as-you-go business models, incorporating spatial analytics, machine learning, blockchain, contracting, operations, and billing opens up a whole new world of data-driven decision making. These next-generation solutions are proactive, based on events that trigger certain actions and built on technology that allows them to not only be more efficient, but also more intelligent. Data is the currency of the digital transformation and data-driven applications are the future of application development. Realizing this vision requires intelligent processes, intelligent experiences, and intelligent analysis.Īs I’ve explained previously, real-time data is one of the central components of the Intelligent Enterprise. Today, an organization has to be both customer-centric and agile, able to respond rapidly to changing market and customer demands.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |