Dataiku vs. Plotly Chart Studio

Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.

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[Plotly Chart Studio is] a great tool to make visualizations on a web based portal. For python developers, it can ease of some burden of writing code again and again for the same thing and also refreshing visualizations on old scripts when data changes. All in all, a simple yet effective tool for your report generation and visualization needs[.]

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• The intuitiveness of this tool is very good.
• Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
• The way you can control things, the set of APIs gives a lot of flexibility to a developer.

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• Connects to almost every kind of data source like SQL, Excel, MS Access, flat files and more.
• Charts get saved to the repository and are re-used by multiple users.
• No need of manual intervention in order to refresh the charts every time the data gets changed.

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• End product deployment.

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• Lack of customer support
• Sometimes fails to visualize extremely large datasets ( probably also due to computer resource constraints)

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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.

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The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.

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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.

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Independently Invited by TrustRadius

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

• Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
• Platform also ease tracking of data processing workflow, unlike Excel.
• Build-in data visualizations covers many use cases with minimal customization; time saver.

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• Improvement in productivity as charts can be shared with teams and feedback can be collected, consolidated and incorporated in the design and charts.
• Charts are aligned with the organization theme and colors and one doesn't need to put in effort every time to do that.
• No upfront capital expenditure and minimum user criteria helps the organization to keep the costs down.

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Independently Invited by TrustRadius

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