KNIME vs Weka comparison

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KNIME

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Executive Summary

We performed a comparison between KNIME and Weka based on real PeerSpot user reviews.

Find out in this report how the two Data Mining solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed KNIME vs. Weka Report (Updated: May 2024).

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KNIME vs. Weka

May 2024

Helped 787,061 peers since 2012

Categories and Ranking

KNIME

Ranking in Data Mining

1st

Average Rating

8.2

Number of Reviews

Ranking in other categories

Data Science Platforms (4th)

Weka

Ranking in Data Mining

2nd

Average Rating

7.6

Number of Reviews

Ranking in other categories

Anomaly Detection Tools (5th)

Market share comparison

As of June 2024, in the Data Mining category, the market share of KNIME is 19.7% and it decreased by 28.7% compared to the previous year. The market share of Weka is 19.7% and it increased by 11.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Mining

Unique Categories:

Data Science Platforms

11.8%

Featured Reviews

AshokPai

Consultant at World Media Group, LLC

Aug 4, 2023

Excellent product with a unique approach, allowing for almost no-code solutions but prebuilt nodes may not always perfectly fit complex needs

One thing to consider is that the prebuilt nodes may not always be a perfect fit for your specific needs, although most of the time, they work quite well. However, if you encounter very complex requirements, you might need to add custom code to achieve your desired outcomes. This is an area that could use some improvement, but the advantage is that it encourages you to evaluate and minimize coding efforts. As a result, you can reduce the overall amount of coding required, which is a positive aspect of KNIME. Another area that could be improved is related to the libraries. While they are quite extensive, they might not always match your exact needs. In such cases, you might have to do some coding to tailor the solution accordingly. Therefore, one area for improvement is the flexibility of prebuilt nodes, as they may not always match complex needs perfectly. Also, enhancing clarity on what the nodes do would be beneficial. For additional features, there are a couple of things that come to mind. Firstly, it would be great to have more clarity on what each node does. Sometimes, it's not very apparent, and additional information would be helpful. Secondly, it would be beneficial to have better ways to interact with and manage nodes, enhancing the user experience. And finally, I think KNIME could improve on how easily it allows for extending functionalities with custom code. Although it's relatively straightforward now, making it even more accessible would be advantageous.

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AwaisAnwar

Treasury Management in Finance Department at National University of Pakistan

Dec 27, 2023

Open source, good for basic data mining use cases except for the visualization results

In my university, we used Weka. Weka was used in marketing by my professor, I was preparing a presentation for PhD proposals specifically on energy consumption and renewable resource utilization. So, I needed data mining tools Weka's is fine for basic results, no big issues there. But if you…

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"KNIME is fast and the visualization provides a lot of clarity. It clarifies your thinking because you can see what's going on with your data."

"This solution is easy to use and it can be used to create any kind of model."

"It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop."

"KNIME is easy to learn."

"The solution is very easy to use"

"The solution is good for teaching, since there is no need to code."

"We leverage KNIME flexibility in order to query data from our database and manipulate them for any ad-hoc business case, before presenting results to stakeholders."

"One of the greatest advantages of KNIME is that it can be used by those without any coding experience. those with no coding background can use it."

More KNIME pros

"It doesn’t cost anything to use the product."

"With clustering, if it's a yes, it's a yes, if it's a no, it's a no. It gives you a 100% level of accuracy of a model that has been trained, and that is in most cases, usually misleading. Classification is highly valuable when done as opposed to clustering."

"It is a stable product."

"There are many options where you can fill all of the data pre-processing options that you can implement when you're importing the data. You can also normalize the data and standardize it in an easier way."

"Weka eliminates the need for coding, allowing you to easily set parameters and complete the majority of the machine learning task with just a few clicks."

"The interface is very good, and the algorithms are the very best."

"The path of machine learning in classification and clustering is useful. The GUI can get you results. No programming is needed. No need to write down your script first or send to your model or input your data."

"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."

More Weka pros

Cons

"I've had some problems integrating KNIME with other solutions."

"One thing to consider is that the prebuilt nodes may not always be a perfect fit for your specific needs, although most of the time, they work quite well."

"The graphic features of KNIME need improvement"

"It's difficult to provide input on the improvement area because it's more of self-learning. However, there are times when I am not able to do certain things. I don't know if it's because the solution doesn't allow me or if it's because of the lack of knowledge."

"KNIME could improve when it comes to large data markets."

"To enhance accessibility and user-friendliness, there is a need for improvements in the interface and usability of deep learning and large-scale learning languages."

"I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports."

"In the last update, KNIME started hiding a lot of the nodes. It doesn't mean hiding, but you need to know what you're looking for. Before that, you had just a tree that you could click, and you could get an overview of what kind of nodes do I have. Right now, it's like you need to know which node you need, and then you can start typing, but it's actually more difficult to find them."

More KNIME cons

"Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."

"I believe is there are a few newer algorithms that are not present in the Weka libraries. Whereas, for example, if I want to have a solution that involves deep learning, so I don't think that Weka has that capability. So in that case I have to use Python for ... predict any algorithms based on deep learning."

"Not particularly user friendly."

"If there are a lot more lines of code, then we should use another language."

"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."

"The filter section lacks some specific transformation tools. If you want to change a variable from a numeric variable to a categorical variable, you don't have a feature that can enable you to change a variable from a numeric variable to a categorical variable."

"In terms of scalability, I think Weka is not prepared to handle a large number of users."

"If you have one missing value in your dataset and this missing value belongs to a specific attribute and the attribute is a numeric attribute and there is only one missing data, whenever you import this data, the problem is that Weka cannot understand that this is a numeric field. It converts everything into a string, and there is no way to convert the string into numerical math. It's really very complicated."

More Weka cons

Pricing and Cost Advice

"The client versions are mostly free, and we pay only for the KNIME server version. It's not a cheap solution."

"Scaling to the on-premises version requires a licensing fee per user that is a bit expensive in comparison to R, Python, and SAS."

"KNIME is a cost-effective solution because it’s free of cost."

"It is free of cost. It is GNU licensed."

"It's an open-source solution."

"With KNIME, you can use the desktop version free of charge as much as you like. I've yet to hit its limits. If I did, I'd have to go to the server version, and for that you have to pay. Fortunately, I don't have to at the moment."

"While there are certain limitations in functionality, you can still utilize it efficiently free of charge."

"At this time, I am using the free version of Knime."

More KNIME pricing and cost advice

"Currently, I am using an open-source version so I don't know much about the price of this solution."

"We use the free version now. My faculty is very small."

"The solution is free and open-source."

"As far as I know, Weka is a freeware tool, and I am not aware if they have an online solution or if it is a commercial product."

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Top Industries

By visitors reading reviews

Manufacturing Company

12%

Financial Services Firm

11%

Computer Software Company

Educational Organization

University

19%

Educational Organization

14%

Computer Software Company

10%

Financial Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about KNIME?

Since KNIME is a no-code platform, it is easy to work with.

What is your experience regarding pricing and costs for KNIME?

We're using the free academic license just locally. I went for KNIME because they have a free academic license. And to be honest, I never bothered to check the prices.

What needs improvement with KNIME?

KNIME is not good at visualization. I would like to see NLQ (Natural language query) and automated visualizations added to KNIME.

What do you like most about Weka?

It is a stable product.

What is your experience regarding pricing and costs for Weka?

Weka is free and open-source software. That is why I used it over KNIME.

What needs improvement with Weka?

I haven't found it particularly useful. It lacks state-of-the-art algorithms and impressive outcomes. While it might offer insights for basic warehouse tasks, it falls short of deeper understanding...

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Product Reports

Download KNIME product report

KNIME

June 2024

Download Weka product report

May 2024

Also Known As

KNIME Analytics Platform

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Knime

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Overview

KNIME is an open-source analytics software used for creating data science that is built on a GUI based workflow, eliminating the need to know code. The solution has an inherent modular workflow approach that documents and stores the analysis process in the same order it was conceived and implemented, while ensuring that intermediate results are always available.

KNIME supports Windows, Linux, and Mac operating systems and is suitable for enterprises of all different sizes. With KNIME, you can perform functions ranging from basic I/O to data manipulations, transformations and data mining. It consolidates all the functions of the entire process into a single workflow. The solution covers all main data wrangling and machine learning techniques, and is based on visual programming.

KNIME Features

KNIME has many valuable key features. Some of the most useful ones include:

Scalability through data handling (intelligent automatic caching of data in the background while maximizing throughput performance)
High extensibility via a well-defined API for plugin extensions
Intuitive user interface
Import/export of workflows
Parallel execution on multi-core systems
Command line version for "headless" batch executions
Activity dashboard
Reporting & statistics
Third-party integrations
Workflow management
Local automation
Metanode linking
Tool blending
Big Data extensions

KNIME Benefits

There are many benefits to implementing KNIME. Some of the biggest advantages the solution offers include:

Integrated Deployment: KNIME’s integrated deployment moves both the selected model, and the entire data model preparation process into production simply and automatically, allowing for continuous optimization in production and also saving time because it eliminates error.

Elastic and Hybrid Execution: KNIME’s elastic and hybrid executions helps you reduce costs while covering periods of high demand, dynamically.

Metadata Mapping: KNIME enables complete metadata mapping of all aspects of your workflow. In addition, KNIME offers blueprint workflows for documenting the nodes, data sources, and libraries used, as well as runtime information.

Guided Analytics: KNIME’s guided analytics applications can be customized based on reusable components.

Powerful analytics, local automation, and workflow difference: KNIME uses advanced predictive and machine learning algorithms to provide you with the analytics you need. In combination with powerful analytics, KNIME’s automation capabilities and workflow difference prepare your organization with the tools you need to make better business decisions.

Supports enterprise-wide data science practices: The deployment and management functionalities of KNIME make it easy to productionize data science applications and services, and deliver usable, reliable, and reproducible insights for the business.

Helps you leverage insights gained from your data: Using KNIME ensures the data science process immediately reflects changing requirements or new insights.

Reviews from Real Users

Below are some reviews and helpful feedback written by PeerSpot users currently using the KNIME solution.

An Emeritus Professor at a university says, “It can read many different file formats. It can very easily tidy up your data, deleting blank rows, and deleting rows where certain columns are missing. It allows you to make lots of changes internally, which you do using JavaScript to put in the conditional. It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured.”

Benedikt S., CEO at SMH - Schwaiger Management Holding GmbH, explains, “All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function. Technical support has been extremely responsive so far. The solution has a very strong and supportive community that shares information and helps each other troubleshoot. The solution is very stable. The initial setup is pretty simple and straightforward.”

Piotr Ś., Test Engineer at ProData Consult, says, “What I like the most is that it works almost out of the box with Random Forest and other Forest nodes.”

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

Sample Customers

Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG

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