IBM Streams (discontinued) vs. IBM StreamSets

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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When you are dealing with a data warehouse and want to find an easy way to integrate applications and expose data in real-time, then IBM StreamSets is the best tool to go for. I'm using it for the same purpose in my applications. This tool will be well-suited for someone with a proper technical background. Though IBM StreamSets UI is mostly drag and drop, advanced configurations require technical expertise or support to do the initial setup.

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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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• It makes building data pipelines quite super intuitive even for non coders.
• Ir also handles real time data ingestion effortlessly so I always have up to date information for my reports.
• It's great at monitoring data quality as well.

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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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• Where the person's skillsets in data analysis is not of an expert.
• Data monitoring and analysis.
• Customer data for better customer acquisition

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IBM Stream sets has been a wonderful addition to our technology stack. It has helped in some of our initiatives such as data engineering, data integration for not only external customers but also for internal purposes. The tool has also helped on our use cases related to streaming data. Moving to another tool would require significant amount of work and time.

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The StreamSets platform is very easy to use and the interface is extremely intuitive. The drag-and-drop, low-code design makes it accessible for teams with varying technical skills, allowing us to quickly connect sources, define transformations, and deploy pipelines without heavy coding. StreamSets allows us to get started quickly and not have to worry about our pipelines breaking once they're built.

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Streamsets support has improved a lot in the last couple of years. We had some challenges in the beginning with support, but now the quality of the support and the responsiveness to tickets are better. We have contacted support multiple times when it came to scenarios where the system was slow or the output as not as we expected

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I was not involved in the implementation

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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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the IBM solution can be considered a good player in the specific perimeter of application because its main functionalities are working well, are easy to use, and complete. it allows also a good degree of freedom when it comes to personalization of pipelines and streams, and customization of data ingestion methodologies.

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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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• it reduced the time I spent managing and updating pipelines when data formats changed
• it saved us from building everything from scratch by making data movement between systems easier
• it helps scale workflows as data volume grows, without much extra effort

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