No-code and low-code data software open the door for users of all backgrounds to fully leverage their organizational data. While RapidMiner and Alteryx both provide software platforms with similar data processing features, they also have their differences. Read on to learn about these two products and how they can help organizations gain the most from their data sets.
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What is RapidMiner?
RapidMiner is a data science software platform that provides features for data preparation, data mining, model development, machine learning, deep learning, text mining, predictive analytics and model operations. The no-code development platform has customizable visual workflow design and automation capabilities.
What is Alteryx?
Alteryx is an analytics automation platform that enables users to access, prepare, analyze, manipulate, blend and output their data. The drag-and-drop data software system also contains features for predictive modeling, machine learning and intuitive workflow development.
RapidMiner vs. Alteryx: Feature comparison
|Data science upskilling||Yes||No|
|Data science capabilities||Yes||Yes|
Head-to-head comparison: RapidMiner vs. Alteryx
RapidMiner has features to help users with their data preparation through its Turbo Prep tool so that it can be ready for analytics and model creation. The software prepares data for predictive modeling, enabling users to explore their data interactively. Through this, users can evaluate their data based on its health, quality, and completeness.
RapidMiner users can also blend multiple datasets within the platform and configure its software to create new columns with the simple expression editor. Once the data has been prepared, users can develop predictive models using the RapidMiner software or export it to their preferred application.
RapidMiner users can save all data prep and ETL pipelines for re-use and automation. A nice factor of this software is that it can easily resolve common problems during the data preparation process, like data quality issues.
Alteryx helps users leverage automation throughout its data preparation processes, eliminating manual processes and human error. The tool helps users with automated data preparation, blending and cleansing features, and provides 300+ no-code or low-code automation building blocks for creating data prep processes.
Alteryx integrates with over 80 different data sources for expansive data prep and analysis capabilities. It provides several tools for simplifying the data prep process, including for automating deletion, reformatting and rearranging data within fields. The Alteryx cleanse tool helps users eliminate small data errors, and the text to columns tool helps with tidying and organizing data into appropriate fields.
The RapidMiner software helps users process their data through machine learning. It has machine learning and model operations features to help their users refine their processes, improve their productivity, and simplify their operations.
RapidMiner’s ML capabilities enable users to develop, evaluate, compare, monitor, manage or swap models simply by using its custom dashboards. Alternatively, users can also use their preferred BI platform for model configuration. RapidMiner uses a containerized architecture and code-free model ops to enable users to gain the most from its ML model predictions in a way that is easy for users of all backgrounds to understand.
An additional RapidMiner feature includes its built-in drift protection, which allows users to monitor their models and identify and address problematic trends.
Alteryx provides automated machine learning capabilities to generate insights, identify key relationships and uncover trends within their data. Users can build machine learning models with the in-product Education Mode, which helps them understand the model generation process that they can easily explain and utilize for decision making.
Alteryx’s Deep Feature Synthesis is an automated feature engineering method that builds on relationships within users’ data. The Alteryx Machine Learning allows users to understand their models’ behavior and predictions. Users can gain more information about the processes by looking at the Shapely Impact Analysis, Feature Importance, Partial Dependency plots and other tools. They can even access the Open Source libraries for greater trust and transparency of their models.
Data science capabilities
RapidMiner has both automated and code-based data science capabilities for users to analyze their data sets fully. Its automated data science capabilities can simplify a wide variety of data processes for its users, including augmented data preparation, predictive model building with Hyperparameter Tuning and Automatic Feature Engineering, risk assessment, target recommendations and code-free features like no-code deployment, automatic monitoring and insight delivery capabilities.
Data scientists who wish to utilize coding can use the RapidMiner code-based features such as a fully integrated coding notebook and open-source libraries. The software also helps bridge the gap between data scientists and non-coders with collaboration tools for easy communication, reusable visual workflows, a governed and flexible coding environment and simplified auditing with a Git-based view of lineage, change history and model explanations.
Alteryx’s software supports data scientists by providing features that help them automate their data processing, deploy working models, compare algorithm performance, train models in a machine learning pipeline and operationalize faster through self-service model deployment.
Alteryx users can assess their data integrity with transparent analytic workflows, and the Alteryx Analytic Process Automation Platform helps them deliver fast results and actionable insights with automated data analytic processing.
Additional features offered through the Alteryx software include data forecasting, predictive analytics, built-in comparison tools to understand different forecasting models, AutoML and Deep Feature Synthesis. Text mining features also help users discover insights in their PDFs and documents by extracting text and helping them gain a deeper understanding of their data with topic modeling, sentiment analysis, and word clouds.
Finally, Alteryx comes with built-in education and community-based learning paths, so Alteryx users can learn about data science as they utilize the software.
Choosing between RapidMiner and Alteryx
RapidMiner may be better for beginners based on its features. It takes on a more collaborative approach to data science while teaching the fundamental building blocks of data processing.
Alteryx, on the other hand, is a highly intuitive tool with a self-service analytics approach to data science that may be better for enterprises that value faster analysis capabilities over upskilling features.