IBM Confluent vs. IBM InfoSphere Information Server vs. IBM StreamSets

If you have a need to stream data, real time or segmented structured data then Confluent is a great platform to do so with. You won't run into packet transfer size limitations that other platforms have. Flexibility in on-prem, cloud, and managed cloud offerings makes it very flexible no matter how you choose to implement.

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Information Server is extremely useful to replace manual developments that require a lot of coding effort. It significantly increases the productivity of the initial development and the future maintenance of the processes since it has a visual development environment with self-documentation.

IBM StreamSets excels in real-time logistics data ingestion and transformation across hybrid systems. It’s less ideal for lightweight ETL tasks or static datasets where simpler tools can achieve similar results with less overhead and complexity.

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• Products work great.
• Training is available.
• Customer support is good.

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• IIS best for ETL ,not ELT , and many and diffrent source systems.
• It also can process big data , unstuctured data
• It is not only DWH , you can use infosphere for analys and see the bigger architecture of your OLTP systems

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• It helps streaming huge data that we have in our Teradata database to various reporting applications that runs on cloud seamlessly.
• We also use IBM StreamSets to power few BI dashboards that our product managers use on regular basis to showcase various data with clients.
• I think the data quality is way better compared to Informatica tool.

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• Cloud based Azure platform features for Confluent lacks behind AWS And GCP

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• I would be nice to have a new web development environment for DataStage.
• Connectivity Packs such as Pack for SAP Application are a little pricey.
• It is confusing for new developers the possibility of developing jobs using different execution engines such as Parallel or Server.

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• The error messages I feel aren t always very descriptive so troubleshooting can take longer
• Maybe more customisation options for scheduling can be done, rest it works pretty well.

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• Scale of implementation
• IBM techsupport

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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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The support from the Confluent platform is great and satisfying. We have been working with Confluent for more than a year now. They sent out resident architects to help us set up Confluent cluster on our cloud and help us troubleshoot problems we have encountered. Overall, it has been a great experience working with the Confluent Platform.

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For our use case it was very important that the technology we were working with fit into our Azure architecture, and met our data processing size requirements to stream data within certain SLAs. Confluent more than met our performance requirements and compared to the others scale options and cost to run it was more than financially viable as a platform solution to our global operations.

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DataStage is more robust and stable than ODI The ability to perform complex transformations or implement business rules is much more developed in DS

First advantage is that this software is particularly new and it keeps updating according to the needs of the user. Other advantage is the it organises and produces conclusions on the basis of data without leaving any relevant information. Other softwares lack in data summarising and readability of the charts and sheets they produce.

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• It enables us to develop event driven application.
• It increases our ability to handle streaming data.
• It reduces latency of communication.

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• Productivity of the development of integration processes.
• Better documentation and governance.
• Reduce training costs of various technologies.

• time saving for automatic collection and integration of data
• time saving thanks to live monitoring and reaction
• time saving for standardization of data

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