The Dataiku platform unifies all data work, from analytics to Generative AI. It can modernize enterprise analytics and accelerate time to insights with visual, cloud-based tooling for data preparation, visualization, and workflow automation.
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Shiny
Score 8.0 out of 10
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Shiny allows users to create data visualization apps, and is designed to be easy to write with. These apps let users interact with data and analyses with R or Python.
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Pricing
Dataiku
Shiny
Editions & Modules
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Pricing Offerings
Dataiku
Shiny
Free Trial
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Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
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Entry-level Setup Fee
No setup fee
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Community Pulse
Dataiku
Shiny
Considered Both Products
Dataiku
Verified User
Anonymous
Chose Dataiku
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the …
Open source availability is a critical factor given licensing cost of other platforms and budget reasons. Secondly, the available features in the community version covers most of the use cases, thus making it comparable or even outdo commercial versions of other software. …
Anaconda is mainly used by professional data scientists who have profound knowledge of Python coding, mainly used for building some new algorithm block or some optimization, then the module will be integrated into the Dataiku pipeline/workflow. While Dataiku can be used by …
Whilst dashboarding may be comparable with some of the other products we evaluated. Nothing compared to the analytical capabilities on offer with Shiny. An added advantage was that we had colleagues knowledgeable in R which meant bringing in Shiny and getting to grips with it …
Shiny is much cheaper to use than Tableau Desktop and Microsoft Power BI. It's not as fancy, and maybe not as effective, but you save lots of money by using Shiny over the previously listed alternatives. The graphs and charts you can make in Shiny are very good for …
Both Tableau and Power BI are easier to learn and allow you to develop dashboards in a faster and more intuitive way, but both have limitations in what you can do with them and if you want to do something more specific it is always more complicated. RStudio is much more …
Shiny can be a good tool in academic but its not upto standard of TMT industry but could possibly be useful in life science industry (which is where its more prevalent usually), its good as its mostly free (not including cost of servers and compute). I would rank its …
Shiny allows easy and fast development of a product into production whereas Jupyter Notebook can be broken really easily by a user. The idea of having a specific server that works with that model is very practical and it's a good advantage. In the contrary, the quantity of …
I would recommend it because it's an amazing tool for different levels of users. From Business Analysts to Data Scientists to Managers, various employees can make use of this tool to make data-driven decisions. I'm not sure about where it would be less appropriate as I'm using it as Data Scientist and so far it pretty much caters to my need.
Shiny is very good for developing dashboards or web applications with specific functionalities. But it is not so easy to use to develop from scratch, it is always better to use another tool to have a general idea of what is expected of a dashboard and then develop the most specific functionalities in Shiny. It is much more flexible than other tools and that is why I consider it to be better for most cases, only that it is more complex to develop or has a longer learning curve.
As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.
The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.
Whilst dashboarding may be comparable with some of the other products we evaluated. Nothing compared to the analytical capabilities on offer with Shiny. An added advantage was that we had colleagues knowledgeable in R which meant bringing in Shiny and getting to grips with it was a lot more seamless and welcomed by the end users.
