No matter how small or big your business is, data is a crucial element in the decision-making process. The benefits of analytics go beyond size and give any organization, including large enterprises, an excellent edge in today’s fast-paced market. Industries of all types are aiming to collect as much data as possible while being wrapped up in competitiveness and ambition. This KNIME vs. RapidMiner analysis can significantly help in the selection process for a platform.

The global big data analytics market is expected to grow with a compound annual growth rate of almost 30% in the coming years, and the revenue is presumed to reach over 68 billion U.S. dollars by 2025 from around 15 billion U.S. dollars in 2019. Companies worldwide are at the dawn of the new era of intelligent analysis systems that take advantage of the potential of artificial intelligence (AI) and machine learning (ML) technologies to analyze data and thus support decision-making, optimize workflows, and free up human effort for predominant tasks.

With data being one of the most valuable assets for businesses, and as operations move online, companies are examining tools and software solutions that will extract practical information hidden in raw data. The key is understanding the type of data that will benefit the business the most, and based on particular characteristics, organizations will create solid plans for investing time and resources toward reaching the ultimate goal.

The blog KNIME vs. Power Bi offers a complete overview of both tools, the solutions they offer, and compatibility with the company’s specific requirements. In this article, we will summarize the advantages of the KNIME analytics platform and compare RapidMiner and KNIME in terms of functionality, ease of use, price range, and value for money, in addition to the pros and cons of each platform.

KNIME vs RapidMiner: Which Is the Superior Option?

There are many software solutions with various features for performing data analysis, data prep, and data mining and choosing the appropriate tool can be tricky and challenging. Both KNIME and RapidMiner have been hailed by Gartner as 2017’s market leaders in data science platforms, so it is high time to compare these two programs.

What Is KNIME?

KNIME Analytics Platform is a free and open source software solution that ensures that users stay on the cutting edge of data science. With over 300 connectors to the data source and all popular machine learning library integrations, KNIME is one of the most prominent specific analysis tools for various industries.

With its ability to access and blend data, transform and analyze, visualize and explore, save and reuse data, and perform data prep, KNIME Analytics Platform is software that creates data science workflows for organizations of all sizes and users with individual data analysis requirements.

KNIME is continuously integrating new developments and making reusable components accessible to everyone to download and use. The KNIME Analytics Platform is intuitive, and the open interface makes it easy for data analysts, developers and IT experts to understand data and design workflows, but at the same time, the simple design and ease of use make this platform suitable for any user without expertise in the field of IT. Additionally, KNIME Hub offers drag-and-drop nodes and components per choice without the need for coding.

Furthermore, KNIME software is excellent for developers who want to model each step of the data analysis process, control the data-related workflows, and ensure that processes are up to date. What is even more impressive is that with the platform’s native nodes, developers can blend tools from different domains into a single workflow which includes scripting in R & Python, machine learning, or connectors to Apache Spark.

The KNIME Analytics Platform key features include support of simple text formats (PDF, CSV, XML, XLS) and unstructured data types (documents, networks, images) or time-series data and offers the possibility for users to open and combine. The series of databases and data warehousing hosted by KNIME allows users to connect data from Microsoft SQL, Oracle, Snowflake, Apache Five, and more. 

This video is a short overview of What is KNIME:

Pros and Cons of KNIME

On the pros side, users elaborate that one of the greatest advantages of KNIME is that anyone can use it without coding, while the most valuable solution is the ability to convert sub-workflows into a node and deploy a solution in a cluster. The compact representation of the graphics makes automation even more helpful. Besides, different individual native nodes can be converted into a single node, and once the workflow is created, any information from various sources can be automatically downloaded. Additionally, KNIME is particularly useful for explaining models in a stepwise manner to team members who don’t have the technical expertise in programming. 

Regarding the drawbacks, users suggest that the dynamic column name feature could be improved in terms of automating processes involving columns that need to be changed. What’s more, KNIME documentation is reported to be not as strong as expected, and beginners could face issues in certain operations. In the field of geospatial data analysis, KNIME lacks specialized modules but still gets the work done.

What Is RapidMiner?

RapidMiner is a Java environment analytics program that went on to become a fast-growing software company with more than 300% growth between 2007 and 2012. RapidMiner is enterprise-ready data science platform has the capacity to amplify the collective impact of people, expertise, and data for breakthrough competitive advantage.

RapidMiner guarantees an entire analytics lifecycle, from machine learning ops, model building, data engineering, data mining and artificial intelligence app building, by ensuring trust, transparency, collaboration, and governance.

The RapidMiner tool is used for extracting insights from unstructured data for a variety of purposes, including improvement of marketing results. Organizations can distinguish customers based on business value with the specific customer lifetime value feature. In addition, RapidMiner has a the customer segmentation option, businesses can segregate their customers by particular characteristics, while the correlations, missing values, and the predictive maintenance feature predict equipment failures and enable cost-effective maintenance. The software effectively identifies fraudulent activities, and in addition, RapidMiner provides quality assurance for resolving issues in the earliest stage.

RapidMiner is a cloud-based and on-premise data science solution that enables businesses to access, load, and analyze structured and unstructured data. The application comes with a visual drag-and-drop tool and machine learning library suitable for developers who want to create and deploy predictive models.

Pros and Cons of RapidMiner

One of the main advantages of RapidMiner is the collaboration and governance features, and users elaborate that the comprehensive platform covers many different processes, such as data extraction and modeling in the ML language. Users describe the community of RapidMiner as reliable and interactive and note that this tool helps businesses make informed decisions using data. Additionally, RapidMiner is described as suitable for profiling and forecasting in any industry. The code-free feature makes this tool great for anyone, meaning users can prepare data and, automatically launch a series of models and analyze the comparisons.

On the cons side, users consider that RapidMiner Studio could improve deep learning by enhancing its features. The lack of video tutorials makes this software challenging to use for beginners. Moreover, users often report software crashes, especially in terms of neural networks.  

KNIME vs RapidMiner: what to choose?

To sum up, RapidMiner is a software platform excellent for data prep, creating predictive models, and operationalizing analytics within business processes. At the same time, KNIME is a free version and open-source data analytics, reporting, and integration platform and the best solution to create and produce data science using one easy and intuitive environment.

Some of the critical features that RapidMiner offers are:

• Graphical user interface
• Analysis processes design
• Multiple data management methods

While KNIME is well-known for providing the following key features:

• Access, merge, and transform all of your data
• Make sense of your data with the tools you choose
• Support enterprise-wide data science practices

KNIME offers Desktop for Mac, Windows, Linux Deployments, and Windows and Linux On-Premise. On the other side, RapidMiner is Cloud, SaaS, and Web-Based and offers Desktop for Mac and Windows, Windows and Linux On-Premise. Both KNIME and RapidMiner are suitable for small, medium, and large companies.

Regarding user support, KNIME has FAQs/Forum and Knowledge Base, while RapidMiner offers more robust support via Email/Help Desk, Phone Support and Chat, FAQs/Forum, and Knowledge Base. 

Closing Thoughts

KNIME is a predominant platform that helps individuals and organizations to make sense of their data. Through the intuitive interface, KNIME bridges the worlds of dashboards and advanced analytics and is an appropriate choice for anyone working with data. While RapidMiner offers a unified user interface to manage various tasks through a graphical drag-and-drop approach and helps businesses leverage their data, KNIME empowers data scientists to be self-sufficient and push the company to the very forefront of technological development and data science by integrating the latest AI and ML techniques.

The detail that might persuade users is the price tag, with RapidMiner being a more expensive solution with $2,500 per user annually for the small version (100,000 data rows and 2 logical processors), $5,000 per user annually for the medium version (1,000,000 data rows and 4 logical processors) and $10,000 per user annually for unlimited access. However, the fact that RapidMiner offers slightly better technical support is something users should hold onto.

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