Demystifying SAP: SAP Data Hub
SAP Data Hub is a data sharing, pipelining, and orchestration solution that helps companies accelerate and expand the flow of data across their modern, diverse data landscapes. SAP Data Hub provides visibility and access to a broad range of data systems and assets; allows the easy and fast creation of powerful, organization-spanning data pipelines; and optimizes data pipeline execution speed with a “push-down” distributed processing approach at each step. SAP Data Hub meets the governance and security needs of the enterprise, ensuring that appropriate policy measures are in place to meet regulatory and corporate requirements.
Why SAP Data Hub?
There is more data and more ways to store and use it than ever before. While this data holds business opportunity, corporate data landscapes are growing increasingly complex, and it is getting harder and costlier for organizations to not only understand the data that they have, but to work across all the different systems that need to use it, and apply end-to-end governance, to capture the maximum value.
Key Pain Points that SAP Data Hub can solve for your business:
Data is kept in silos (files, Hadoop, Data Warehouses, etc.) across the enterprise. Users can’t access and work with the data they need across the silos where it’s stored. In particular, it is complex, time consuming, and costly to connect Big Data with enterprise data and business processes to gain insight and value from it.
End-to-end data governance required across complex landscapes: The need to manage and govern data across a landscape is well understood. Ensuring data lineage and impact analysis of changes, managing security and privacy requirements, etc. are all critical aspects of a trusted enterprise landscape. With the increased complexity of enterprise landscapes, which can now include Hadoop data lakes, EDWs, Cloud storage, enterprise apps, etc., the ability to appropriately provide effective governance is more difficult. Without end-to-end governance across all data sources, organizations cannot trust and rely on the data’s accuracy, creating risk for anyone using analytics or operational applications that use the data.
Big Data technologies lack enterprise readiness: Businesses generally cannot solve the complexity of their landscape simply by storing all their data in a Hadoop data lake. Hadoop solutions, while powerful, often do not have the extent of governance and security measures that enterprises require. Data lakes often have limited governance for Big Data initiatives, little automation to schedule processing in the landscape, fragmented monitoring and tracing capabilities of individual technologies, and lack common security and access management.
Currently available tools require high effort to productize data scenarios across the enterprise. Many integration tools today are point to point, require highly trained resources to execute, and are highly manual. This makes it challenging to rapidly connect and implement desired data outcomes.
Specialized skill sets are often needed to implement, scale and create value out of Big Data initiatives. These specialized resources are often difficult to find and difficult to retain.
SAP Data Hub accelerates and expands your data projects by easily and quickly creating powerful data pipelines in a single, visual design environment
In a single design environment, data stewards can easily and quickly create powerful data pipelines that access, harmonize, transform, process, and move information from a variety of sources across the organization. Pipeline creators can easily activate powerful libraries for computation or machine learning, for example; rapidly connect data of a wide variety of types, such as social media, customer, and product information; and leverage existing processing investments, such as capabilities in SAP HANA, Apache Hadoop, SAP Vora, or Apache Spark. Pipeline models can be easily copied, modified, and re-used to accelerate pipeline deployment and leverage best practices.
SAP Data Hub accelerates business results with innovative “push-down” processing to power more agile, comprehensive data-driven applications
SAP Data Hub not only accelerates the creation and management of data pipelines that span varied data sources, it also provides fast execution of the pipeline activities themselves by distributing computational tasks to the native environments where the data reside. This federated “push-down” distributed processing ensures that the activities of the pipeline complete as rapidly as possible, delivering fast results to the business. This data processing approach allows customers to take advantage of serverless computing in the cloud, potentially reducing the overall cost of data pipelining and data management.
How is this part of SAP Leonardo?
SAP Leonardo is a digital innovation system that enables customers to rapidly innovate and then rapidly scale that innovation to redefine their business for the digital world. SAP’s Big Data solutions, SAP Data Hub, SAP Vora, and SAP Cloud Platform Big Data Services, are relevant to the Leonardo offering because they are key to scale and innovation. As such, they are offered in the Leonardo Big Data packages.
SAP Data Hub resonates with the core themes of Leonardo, because:
It minimizes risk and disruption. It works with your existing data landscape and doesn’t require you to centralize data.
It maximizes your existing technology investments and allows you to make the most of them – it plays the data where it lays and it utilizes the processing capabilities closest to the data, so that your data pipelines complete as quickly as possible.
It allows you to rapidly scale innovation, since it makes data pipelining capability available to a broader range of users within your organization, and it allows you to easily build on successes.
It allows you to be open to the future. Due to its open architecture, not only do you leverage the most of your data from today, no matter in the cloud, on premises, SAP solution or non-SAP solution, you can also quickly and easily adopt new advances, such as in machine learning and the next data analytics or processing innovation.
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