In part 1 of this blog series I talked about how data processing landscapes are getting more complex and heterogeneous creating roadblocks for customer who want to adopt truly hybrid cloud data applications. In the beginning of this year, Cisco and SAP decided to join forces and to bring the SAP Data Hub to the Cisco Container Platform. The goal is to provide a real end-to-end solution to help customers tackle the challenges described above and enable them to become a successful intelligent enterprise. We are focusing on providing a turn-key enterprise-scale solution that fosters a seamless interplay of powerful hardware and sophisticated software.
Figure 1 Unified data integration and orchestration for enterprise data landscapes.
SAP brings into the game its novel data orchestration and refinery solution ‘SAP Data Hub’. The solution brings a number of features that allow customers to manage and process data in complex data landscapes involving on-premise systems and across multiple clouds. SAP Data Hub supports connecting the different systems in a landscape to a central hub to gain a first overview of all systems involved in data processing within a company. Above that the Data Hub is able to scan, profile and crawl those sources to retrieve the metadata and characteristics of the data stored in those sources. With that the SAP Data Hub provides a holistic data landscape overview in a central catalog and allows companies to answer the central questions about data positioning and governance.
Furthermore, the SAP Data Hub allows the definition of data pipelines that allow a data processing and landscape orchestration across all connected systems. Data pipelines consist of operators—small independent computation units—that form a joint computation graph. The functionality an operator provides can reach from very simple read operations and transformations (e.g. change the date format from US to EU), over interacting with a connected system, towards invoking a complex machine learning model. The operators are invoking their functionality and applying their transformations, while the data flows through the defined pipeline. This kind of data processing changes the paradigm of static, transactional ETL processes to a more dynamic flow-based data processing model.
With all of this functionality, we kept in mind that for being successful in bridging enterprise data and big data, we need to be open with respect to connecting not only SAP enterprise systems, but common systems used within the Big Data space (compare Figure 2). For this purpose, the SAP Data Hub is focusing on an open connectivity paradigm providing a huge number of connectors to different kinds of cloud and on-premise data management systems fostering the integration between enterprise data and big data.
All of that makes the SAP Data Hub a powerful enterprise application that allows customer to orchestrate and manage their complex system landscape. However, a solution like the Data Hub would be nothing without a powerful and flexible platform. Customers are increasingly turning towards containerized applications and Kubernetes as the orchestrator of choice, to handle the requirements to efficiently process large volumes of data. For this reason, it was a clear decision to move the SAP Data Hub also in this direction. The SAP Data Hub is completely containerized and uses Kubernetes as its platform and foundation.
Figure 2 SAP & Cisco delivering turn-key solutions for complex enterprise data landscapes.
This is where Cisco with its advanced Cisco Container Platform (CCP) on its hyperconverged hardware solution Cisco Hyperflex comes into the game. Providing elastically scalable container clusters as a single turnkey solution covering on-premise and cloud environments with a single infrastructure stack is key for enterprise customer involved in big data analytics. With the Cisco Container Platform on Hyperflex 3.0 Cisco offers a fully integrated and flexible ‘container as a service’ offering with lifecycle management for hardware and software components. It provides a 100% upstream Kubernetes with integrated networking, system management and security. In addition, it utilizes modern technologies such as ‘istio’ and ‘Cloud Connect VPN’ to efficiently bridge on-premise and cloud services from different cloud providers. Accordingly, it accelerates a cloud-native transformation and application delivery in hybrid cloud enterprise environments, clearly embracing the multi-cloud world and helping to solve the multi-cloud challenges. Furthermore, the CCP allows to monitor the entire hardware and Kubernetes platform to allow customers to identify issues and non-beneficial usage patterns pro-actively and troubleshoot container clusters with fast pace.
Accordingly, the CCP is the perfect foundation for deploying the SAP Data Hub in complex, multi-cloud and hybrid cloud customer landscapes. We complemented the solution with Scality Ring an enterprise-ready scale-out file and object storage that fulfills major characteristics for production-ready usage; e.g. guaranteed reliability, availability and durability. This adds a data lake to the on-premise solution allowing price-efficient storage for mass data. In addition, we added network traffic load balancing with the advanced AVI Networks load balancers. They provide intelligent automation and monitoring for improved routing decisions. Both additions greatly benefit the CCP and complete it towards a full big data management and processing foundation.
With the release of the SAP Data Hub on the Cisco Container Platform running on Hyperflex 3.0 and complemented with Scality Ring and AVI Networks load balancers during SAP TechEd Las Vegas, customers will have the option to receive a turn-key, full-stack solution to tackle the challenges of modern enterprise data landscapes. They can start fast, they remain flexible and they receive full-stack support from Cisco’s world class engineering support and SAP’s advanced support services. Accordingly, SAP and Cisco together enable customers to win the race for the best data processing in the digital economy.
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