Azure Stream Analytics vs. IBM StreamSets

Data enrichment is effectively done in stream analytics also checking the values with different functionality like windowing and group by clause is effectively working.

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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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• Routing of data from multiple inputs to multiple output
• You create your own user define function.
• Intermediate query is working very effectively.

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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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• Code competency is not that much effective
• Ml models can't be integrated with stream analytics

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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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Azure Stream Analytics is easy to implement and also to integrate compare to other services like iot analytics

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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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• Very nice roi while using it.
• Multiple integration is the best functionality

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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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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info