Dataiku vs. RapidMiner

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.

Incentivized

RapidMiner is the best tool to build models on textual data. It is rich in ML algorithms and reduces the need to manually tune the parameters. It automatically optimizes them, thus providing a better solution. RapidMiner again extends great capability for data preparation, its insane connections to almost every data source pulls in the data easily into one environment. And it can comfortably perform data cleaning and process tasks over that. RapidMiner is not so good with image, audio or video data. These data points cannot be used directly in their raw form. They must be transformed into some intermediate form for performing analytics over it. Moreover, there are no connectors to directly pull data from their varied sources. For example, we don't have a connector to read audio data directly from a switch and then convert it to text (although Google speech API is available for audio to text conversion.)

Incentivized

• Very intuitive and easy to use UI, making a lot of types of users can collaborate with each other easily, by visualizing the same workflow.
• Many building blocks can be reused immediately, avoid a lot of non-standard boiler plate implementation.
• Data pre-analysis and feature engineering assistance increase the productivity as well as the efficiency of data scientists.
• Many data connectors support wide range of data storage, from SQL, TeraData, Hadoop Hive, etc.
• Support from research till final MaaS solution deployment.

Incentivized

• RapidMiner Studio offers a superb user interface with an intuitive workflow paradigm that is very easy to learn.
• RapidMiner Studio’s operators make it a complete and powerful tool for data preprocessing, data visualization, and data mining/analytics.
• RapidMiner Studio provides excellent documentation, countless worked examples, training and support via a large user community.
• Every problem is solved using a sequence of operators.
• Statistical analysis capabilities offered with the T-Test, ANOVA, Grouped ANOVA, and ANOVA Matrix operators.
• Textual data mining operators.
• Web-based and cloud computing capabilities.
• Visualization capabilities.
• Marketplace Extensions – especially Finance And Economics.
• Process portability.

Incentivized

• Its community support is very limited at the moment
• Complex to integrate with automation tools such as Blue Prism

Incentivized

• Wish the tool was more efficient in terms of processing power. The tool takes a lot of CPU processing power, even for a small process on a small data set
• Wish there were more options on charts and graphs to visualize the data

Incentivized

Very fast and user-friendly tool

Incentivized

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.

Incentivized

Very use to use and learn

Incentivized

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.

Incentivized

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.

Incentivized

The other product like RapidMiner Studio that I have used is WEKA. I decided to use RapidMiner because almost all modelling methods and feature selection methods from the Weka machine learning library are available within RapidMiner. Furthermore, RapidMiner Studio is a visual workflow and therefore it is easier to demonstrate and visualise the processes involves in getting the desired results. Visualization of workflow enhances teaching and learning. RapidMiner is rich with algorithms and online learning materials that can assist students in their self-directed learning on data preparation, machine learning, deep learning, text mining, and predictive analytics. Moreover, RapidMiner repository has more than 1500 machine learning algorithms and functions that students can explore for any case study and assignments. The RapidMIner is also an open platform that can seamlessly integrates with other applications programmed with other programming languages like R and Python.

Incentivized

• 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

• We saved over $100k on our direct mail program by not mailing to those unlikely to respond to our mailings based on our predictive analysis.
• Our CX team has saved countless hours by automating call scripts to isolate key phrases and code each call.

Incentivized