SAP HANA is intelligent, agile, and efficient by design, but this past year we have added new innovations across the board to allow you to unlock the value of your data, enabling our customers to act with intelligence, modernize for agility, and scale cost-efficiently.
It is intelligent. Not only it has new capabilities to help IT accelerate operations, and data scientists shorten ML lifecycles but most fundamentally its unique next-generation hybrid transactional analytical core can provide accurate answers in real-time on live data without tuning. SAP HANA also uniquely processes and analyzes diverse data in context in one system to always provide full situational awareness in real-time.
It is also agile to allows you to securely manage, access and share data across clouds and on-premise, enabling you to innovate faster on the best infrastructure for the job at hand. SAP has a number of innovations here across the whole spectrum - from new support for software managed Hyperconverged Infrastructure to enhanced anonymization and end-to-end modeling for compliance enforcement to faster innovation with improved developer experience across cloud and on-premise.
Finally, SAP HANA is efficient – this is something our customers agree on. A recent IDC survey found that it is 42% faster to deliver solutions with SAP HANA than with other traditional databases or special-purpose databases. SAP's all-in-one architecture has also allowed our customers to increase productivity by 5x.
SAP HANA is intelligent by design with its ability to process hybrid transactional analytic workloads on multimodel data in one system without tuning.
For system administrators and DBAs, SAP HANA Cockpit advises on optimization of system operations by offering recommendations for tasks such as SQL optimizations, optimal warm data placement across data tiers, and performance-optimized distribution of tables. These recommendations are based on the analysis of real-life operations patterns and dramatically simplify operations for large scale deployments. Furthermore, enhancements to workload management add fine-grain control over the allocation and management of resources to improve the scalability of large HTAP deployments.
For data scientists, now SAP HANA accelerates ML lifecycles in a number of ways. For example, new APIs allow R and Python developers to leverage SAP ML capabilities directly from their development environments. It is now possible to manage the lifecycle of Tensorflow models directly in SAP HANA, where the bulk of your data resides, and you build your applications. Furthermore, it also possible to use R scripts in streaming analytics algorithms.
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