Oracle Coherence vs. SAP Data Intelligence (legacy)

Oracle Coherence can be used to solve latency problems by caching data near the application tier. In-memory performance helps to reduce data contention, thus improving application response time. Oracle Coherence does a great job at scaling linearly and can do so dynamically. Oracle Coherence can replicate data so it can be part of a disaster recovery solution. One thing that is often overlooked with elastic caches is their ability to analyze data in memory, leveraging the processing power of the data grid. This is something Oracle Coherence does exceptionally well. Oracle Coherence also provided event handling capabilities to allow applications to respond to events triggered by transactions.

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If you have an SAP products ecosystem in your IT landscape, it becomes a no-brainer to go ahead with an SAP Data Intelligence product for your data orchestration, data management, and advanced data analytics needs, such as data preparation for your AI/ML processes. It provides a seamless integration with other SAP products.

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• Performance. Superfast resolution of complex data grid queries.
• Reliability. Availability domain support for disaster recovery.
• Programming model. Frictionless API for use with Java 8.

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• It integrates well with our current ecosystem of SAP products, like HANA.
• It provides end-to-end machine learning operations, with tools for the complete model life cycle.
• It has a simple user interface for novice users, with complex tools also available for power users.
• It builds on SAP Data Hub, providing all the ETL functions of that tool with additional machine learning functionality.
• It can run in the cloud, no on-premise software management needed.
• Many programming languages are supported, it provides a sandbox environment for the user to develop in whichever style they prefer.
• SAP is very actively developing and improving it.

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• I can't really come up with too many flaws, but possibly a bit expensive considering there are good open source caches like Ehcache.

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• Data transfer speed tends to be slow when there is poor internet connection since SAP Data Intelligence don’t synchronize data while offline. However, this is not vendor fault, that’s why we have implemented robust wireless technology internet connection in our organization.

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Allow collaborations among various personas
with insights as ratings and comments on the
datasets Reuse knowledges on the datasets for new users Next-Gen Data Management and Artificial Intelligence

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I think the troubleshooting process might be streamlined with improved error recording and tracing. A lot of information about issues and how to fix them is hidden away in the Kubernetes pods themselves. I'm not sure whether SAP Data Intelligence can fix this problem it may be connected to Kubernetes's design, in which case fixing it could need modifications inside Kubernetes itself.

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Oracle Coherence support team is responsive and knowledgeable. We contacted them to ask a couple of design questions about how we were setting up Oracle Coherence based on how we used IBM's Datapower Extreme Scale. They were able to guide us so that we got the design correct the first time and didn't have to go back and re-architect our design later.

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Initially we struggle to get help from SAP but then dedicated Dev angel was assigned to us and that simplify the overall support scenario. There is still room of improvement in documentation around SAP Data intelligence. We struggle a lot to initially understand the feature and required help around performance improvement area,

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DataSynapse GridServer has the same cache functionality connected with a grid distributed application deployment platform. It provides all the necessary tools to configure and manage these applications. On the otherhand Oracle Coherence has more a flexible configuration and better performance. Also the Oracle configuration and monitoring tools are more convenient and informative than the DataSynapse ones.

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One of the reasons to pick SAP Data Intelligence is the speed and security it provides, in addition to the excellent support it provides. It is also compatible with all popular databases, which is another reason to choose it.

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• In my current project, it is yet to be decided as we are on the way to make it live for users. But I am sure it will be an added value to obtain timely calculations.

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• Automation of data management slashed tasks by over 60% in most departments for the first 8 months.
• Metadata catalogs have enabled us to categorize data from disjointed sources in one place.
• It runs multiple ML models which enhances flexibility when managing data.

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