IBM Streams (discontinued) vs. Oracle Coherence

Like the name says, it is good for streaming data and analyzing. It is great to look at tuples at a fast rate, filtering, calling other sources to enrich data, can call APIs, etc. Could do better for ingest use cases, can do better with guaranteed delivery, etc.

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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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• IBM Streams is well suited for providing wire-speed real-time end-to-end processing with sub-millisecond latency.
• Streams is amazingly computationally efficient. In other words, you can typically do much more processing with a given amount of hardware than other technologies. In a recent linear-road benchmark Streams based application was able to provide greater capability than the Hadoop-based implementation using 10x less hardware. So even when latency isn't critical, using Streams might still make sense for reducing operational cost.
• Streams comes out of the box with a large and comprehensive set of tested and optimized toolkits. Leveraging these toolkits not only reduces the development time and cost but also helps reduce project risk by eliminating the need for custom code which likely has not seen as much time in test or production.
• In addition to the out of the box toolkits, there is an active developer community contributing additional specialized packages.

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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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• Documentation could be more extensive, with more examples, although overall this is not too bad compared to some of the alternative solutions.
• Seems expensive to use in production.

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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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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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There are well explained tutorials to get the user started. If you are looking for business application ideas, the user community offers a diversity of applications. It is very easy to launch applications on the cloud and can integrate with other analytic tools available on Watson Studio. It takes away the burden of the technology so that users can focus on business innovations.

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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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• Ability to do more with less
• Admins and data analyst can now focus on more thinking tasks
• No negative impacts yet

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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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