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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    IBM StreamSets

    Score7.9 out of 10
    N/AIBM® StreamSets enables users to create and manage smart streaming data pipelines through a graphical interface, facilitating data integration across hybrid and multicloud environments. IBM StreamSets can support millions of data pipelines for analytics, applications and hybrid integration.N/A

    Keras

    Score7 out of 10
    N/AKeras is a Python deep learning libraryN/A
    Pricing
    IBM StreamSetsKeras
    Editions & Modules
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    Offerings
    Pricing Offerings
    IBM StreamSetsKeras
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    IBM StreamSetsKeras
    Considered Both Products
    IBM
    No answer on this topic
    Open Source
    No answer on this topic
    Key User Insights
    Would buy again
    100%
    Would buy again
    9 Answers
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    Delivers good value for the price
    100%
    Delivers good value for the price
    9 Answers
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    Happy with the feature set
    100%
    Happy with the feature set
    9 Answers
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    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    5 Answers
    No answers on this topic
    Implementation went as expected
    100%
    Implementation went as expected
    8 Answers
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    Best Alternatives
    IBM StreamSetsKeras
    Small Businesses
    No answers on this topic
    TensorFlow
    Score7.6 out of 10
    Medium-sized Companies
    Apache Spark
    Score8.8 out of 10
    Google Cloud AI
    Score8.7 out of 10
    Enterprises
    Apache Spark
    Score8.8 out of 10
    Google Cloud AI
    Score8.7 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    IBM StreamSetsKeras
    Likelihood to Recommend
    7.3
    (9 ratings)
    8.1
    (6 ratings)
    Usability
    7.7
    (8 ratings)
    7.7
    (2 ratings)
    Support Rating
    -
    (0 ratings)
    8.2
    (2 ratings)
    User Testimonials
    IBM StreamSetsKeras
    Likelihood to Recommend
    IBM
    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.
    Incentivized
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    Open Source
    Keras is quite perfect, if the aim is to build the standard Deep Learning model, and materialize it to serve the real business use case, while it is not suitable if the purpose is for research and a lot of non-standard try out and customization are required, in that case either directly goes to low level TensorFlow API or Pytorch
    Incentivized
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    Pros
    IBM
    • 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.
    Incentivized
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    Open Source
    • One of the reason to use Keras is that it is easy to use. Implementing neural network is very easy in this, with just one line of code we can add one layer in the neural network with all it's configurations.
    • It provides lot of inbuilt thing like cov2d, conv2D, maxPooling layers. So it makes fast development as you don't need to write everything on your own. It comes with lot of data processing libraries in it like one hot encoder which also makes your development easy and fast.
    • It also provides functionality to develop models on mobile device.
    Incentivized
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    Cons
    IBM
    • 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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    Open Source
    • As it is a kind of wrapper library it won't allow you to modify everything of its backend
    • Unlike other deep learning libraries, it lacks a pre-defined trained model to use
    • Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs
    Incentivized
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    Usability
    IBM
    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.
    Incentivized
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    Open Source
    I am giving this rating depending on my experience so far with Keras, I didn't face any issue far. I would like to recommend it to the new developers.
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    Support Rating
    IBM
    No answers on this topic
    Open Source
    Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.
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    Alternatives Considered
    IBM
    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.
    Incentivized
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    Open Source
    Keras is good to develop deep learning models. As compared to TensorFlow, it's easy to write code in Keras. You have more power with TensorFlow but also have a high error rate because you have to configure everything by your own. And as compared to MATLAB, I will always prefer Keras as it is easy and powerful as well.
    Incentivized
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    Return on Investment
    IBM
    • time saving for automatic collection and integration of data
    • time saving thanks to live monitoring and reaction
    • time saving for standardization of data
    Incentivized
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    Open Source
    • Easy and faster way to develop neural network.
    • It would be much better if it is available in Java.
    • It doesn't allow you to modify the internal things.
    Incentivized
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