Altair SLC vs. Dataiku

WPS Analytics allowed our company's business users to keep using their existing programs while getting similar results with much less learning time. It's a great accomplishment when software for business users is changed—excellent relationships with providers and other salespeople. As a SAS user, I needed an interpreter for the SAS language, and WPS has more packages and a lower price than any other company I could find, so I decided to go with
it.

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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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• WPS Analytics is powered with high performance data processing engine.
• Reusable data models save finances and time.
• Self-service data analyzation portals.

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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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• Currently, I barely encounter challenges when using WPS Analytics although I don't use it on a daily basis. There's no functionality or feature that needs more improvement.

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• End product deployment.

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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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I use a lot of data discovery and visualization tools at workstations to help me gain deep insights into data. WPS Analytics doesn't vary much from all the DVD tools that I have listed, I always go for the best.

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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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• Using machine learning and natural language to analyze data saves time and effort.
• Standard reporting and business intelligence help us reduce workload and make data-driven decisions.
• Drag-and-drop workflow saves time when different languages like SQL and SAS make it easier to code and prepare data.

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• 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.

Incentivized

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