KNIME Business Hub vs SAP Predictive Analytics [EOL] comparison

Knime and SAP are both solutions in the Data Science Platforms category. Knime is ranked #4 with an average rating of 7.9, while SAP is ranked #27. Knime holds a 5.8% mindshare in Data Science Platforms, compared to SAP’s 1.4% mindshare. Additionally, 94% of Knime users are willing to recommend the solution, compared to 100% of SAP users who would recommend it.

KNIME Business Hub

Read 62 KNIME Business Hub reviews

8,453 Views
6,255 Comparison Views

94% willing to recommend

SAP Predictive Analytics [EOL]

Read 3 SAP Predictive Analytics [EOL] reviews

783 Views
775 Comparison Views

100% willing to recommend

KNIME Business Hub

SAP Predictive Analytics [EOL]

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Executive SummaryUpdated on Apr 15, 2026

KNIME Business Hub and SAP Predictive Analytics [EOL] compete in the data analytics sector. KNIME Business Hub appears to have an advantage due to its support and cost-effectiveness, but SAP Predictive Analytics [EOL] offers more sophisticated features, which is a significant value add.

Features: KNIME Business Hub offers collaborative capabilities and easy integration with multiple data sources. It includes an extensive library of machine learning algorithms. Alternatively, SAP Predictive Analytics [EOL]'s strength lies in predictive modeling with advanced automation.

Ease of Deployment and Customer Service: KNIME Business Hub uses a straightforward cloud deployment model, combined with strong, responsive customer support. SAP Predictive Analytics [EOL] involves a complex on-premise setup but offers extensive customization options.

Pricing and ROI: KNIME Business Hub maintains a competitive pricing structure with a promising ROI due to low initial costs. On the other hand, SAP Predictive Analytics [EOL] requires more substantial upfront investment, yet provides a longer-term return on investment due to its advanced predictive analytics.

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April 2026

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Review summaries and opinions

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

Categories and Ranking

KNIME Business Hub

Ranking in Data Science Platforms

4th

Average Rating

8.2

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Data Mining (1st)

SAP Predictive Analytics [EOL]

Ranking in Data Science Platforms

27th

Average Rating

8.6

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2026, in the Data Science Platforms category, the mindshare of KNIME Business Hub is 5.8%, down from 11.8% compared to the previous year. The mindshare of SAP Predictive Analytics [EOL] is 1.4%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Mindshare Distribution
Product	Mindshare (%)
KNIME Business Hub	5.8%
SAP Predictive Analytics	1.4%
Other	92.8%

Data Science Platforms

Featured Reviews

NataliaRaffo

Co Founder & Chief Data Officer Cdo at NTT DATA

Workflow automation has accelerated advanced analytics and machine learning delivery

Sometimes it is a little bit difficult to use some nodes when we have many large-scale data, for example, CSV files with a large amount of data. It is sometimes difficult to try to import the data in KNIME Business Hub nodes because I think that some features that are in the CSV in text, for example, large text, is difficult for KNIME Business Hub to import these fields. I don't know why, but it is very difficult. We need to try to use different nodes for importing the data, such as File Reader and CSV Reader. However, I think that it is always the features that have much text, it is difficult for KNIME Business Hub to understand and import this information. I don't know why, or maybe I don't know if we don't know what the better option is to configure the node to import all the CSV or the data set. However, we have always had this problem. In some nodes, sometimes it is the same because sometimes, for example, I have a CSV and in my CSV, I have a feature that is, for example, a date. When I import this data set in the File Reader node, I have problems with this field because it is a date, but the problem is that it imports it as text, for example. We try to use their nodes that convert text to date, but sometimes it is difficult, and it is not immediate to transform the text into a date. So we needed to convert the text into a date in the CSV, and then import it again in the KNIME Business Hub node and try to have a good read of this field. I know that KNIME Business Hub has some nodes to convert text to date and others, but sometimes it is difficult to use these nodes. I don't know why. Maybe it needs a specific format for the date and we need to transform our feature in this option. So sometimes it is a large process to convert these features. However, sometimes we need to investigate and search for other nodes, and try with other nodes to import these cases.

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Gary Cook

Executive at Empowered Analytics

Enables us to forecast and pull trends and has an easy installation

My rating for SAP Predictive Analytics would be an eight out of ten. If I have to be bold, I'll probably say that we're building away hours, and we are actually putting a lot of the actual predicting stuff back into the warehouse. So running it very bi-directionally. So I'm not sure what its integration features are at the moment, but that's an area we're going to look into in the next month or so.

Read full review

Quotes from Members

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

Pros

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

"I've never had any problems with stability."

"It's a coding-less opportunity to use AI. This is the major value for me."

"This solution is easy to use and especially good at data preparation and wrapping."

"For organizations with a small team of data analysts or data scientists, it is a very easy tool to become familiar with predictive modeling, and makes it possible to hand over projects to colleagues without the need to extensively document them."

"From a user-friendliness perspective, it's a great tool."

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

"We have been able to appreciate the considerable reduction in prototyping time."

More KNIME Business Hub pros

"I have found that the solution is very stable."

"I think the features of the actual ability to forecast and pull trends and correlations has been really good."

"The most valuable features are the analytics and reporting."

"We always purchase SAP support because it is very good."

"SAP Predictive Analytics is better suited for business users because it hides the complexity of the model, whereas Microsoft Azure Machine Learning provides a lot more flexibility for technical professionals to tweak the model."

Cons

"It could input more data acquisitions from other sources and it is difficult to combine with Python."

"It's very general in terms of architecture, and as a result, it doesn't support efficient running. That is, the speed needs to be improved."

"The overall user experience feels unpolished; data field type conversion is a real hassle, and date fields are a hassle; documentation is pretty poor; user community is average at best."

"Sometimes it is a little bit difficult to use some nodes when we have large-scale data, for example CSV files with a large amount of data, because some features that are in the CSV as text, for example large text, are difficult for KNIME Business Hub to import, and we need to try to use different nodes for importing the data, such as File Reader and CSV Reader."

"The solution is inconvenient when it comes to wrangling data that includes multiple steps or features because each step or feature requires its own icon."

"The ability to handle large amounts of data and performance in processing need to be improved."

"I would like to see better web scraping because every time I tried, it was not up to par, although you can use Python script."

"KNIME doesn't handle large datasets or a high number of records well."

More KNIME Business Hub cons

"This solution works for acquired data but not live, real-time data."

"The license fee appears to be prohibitively expensive and overly secretive, leading our clients to opt for cloud-based solutions that only charge for data storage and processing time."

"This solution works for acquired data but not live, real-time data."

Pricing and Cost Advice

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

"KNIME is free as a stand-alone desktop-based platform but if you want to get a KNIME server then you can find the cost on their website."

"It is expensive to procure the license."

"For beginners, the free desktop version is very attractive, but the full server version can be more expensive. I have only used the free version and it offers a fair pricing system. I have been promoting it to others without any compensation or request from the company, simply because I am enthusiastic about it. I am not aware of the pricing for the server version, but it seems to be widely used."

"KNIME assets are stand alone, as the solution is open source."

"We're using the free academic license just locally. I went for KNIME because they have a free academic license."

"KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required."

"KNIME is an open-source tool, so it's free to use."

More KNIME Business Hub pricing and cost advice

"A free trial version is available for testing out this solution."

"The pricing is reasonable"

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Manufacturing Company

University

Educational Organization

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	20
Midsize Enterprise	16
Large Enterprise	31

No data available

Questions from the Community

What is your experience regarding pricing and costs for KNIME?

I rate the product’s pricing a seven out of ten, where one is cheap and ten is expensive.

See all answers

What needs improvement with KNIME?

Regarding integration capabilities, I do not think it is that easy to integrate KNIME Business Hub with another product because the connector does not have many options. For example, if I want to c...

See all answers

What is your primary use case for KNIME?

My use case for KNIME Business Hub includes automation, querying from the database, and outputting to Excel and creating charts.

See all answers

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Compared 6% of the time

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Product Reports

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KNIME Business Hub

April 2026

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Buyer's Guide

Data Science Platforms

April 2026

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Also Known As

KNIME Analytics Platform

SAP BusinessObjects Predictive Analytics, BusinessObjects Predictive Analytics, BOPA

Overview

KNIME Business Hub offers a no-code interface for data preparation and integration, making analytics and machine learning accessible. Its extensive node library allows seamless workflow execution across various data tasks.

KNIME Business Hub stands out for its user-friendly, no-code platform, promoting efficient data preparation and integration, even with Python and R. Its node library covers extensive data processes from ETL to machine learning. Community support aids users, enhancing productivity with minimal coding. However, its visualization, documentation, and interface require refinement. Larger data tasks face performance hurdles, demanding enhanced cloud connectivity and library expansions for deep learning efficiencies.

What are the most important features of KNIME Business Hub?

No-Code Interface: Simplifies data preparation without coding expertise.
Node Library: Supports diverse data tasks, including ETL and machine learning.
Python and R Integration: Seamlessly connects with popular programming languages.
Automation Capabilities: Enhances workflow productivity.
Visual Workflow Tools: Offers efficient data handling with visual aids.

What benefits and ROI can users expect from KNIME Business Hub?

Increased Productivity: Streamlined data tasks reduce development time.
Community Support: Access to extensive resources and user assistance.
Cost-Effective: Robust performance even on standard hardware.
Accessible Learning Curve: Simplicity allows easy access for educational and professional use.
Enhanced Prototyping: Ideal for decision model testing.

KNIME Business Hub finds application in data transformation, cleansing, and multi-source integration for analytics and reporting. Companies utilize it for predictive modeling, clustering, classification, machine learning, and automating workflows. Its coding-free approach suits educational and professional settings, assisting industries in data wrangling, ETLs, and prototyping decision models.

Knime

SAP Predictive Analytics [EOL] offered a powerful platform for creating predictive models that supported business decision-making by utilizing historical data to anticipate future trends.

SAP Predictive Analytics [EOL] was designed to integrate with existing SAP environments, allowing businesses to leverage their existing data infrastructure. It provided users with intuitive tools to automate data preparation and model management, simplifying complex analytical processes. Data scientists could efficiently build and deploy predictive models to address specific business questions. SAP emphasized ease of deployment and scalability, ensuring the platform met the needs of data-driven enterprises.

What are the key features?

Automated Model Creation: Streamlines the process of generating predictive models, reducing time and effort.
Data Preparation: Simplifies data cleaning and preparation, ensuring high-quality input for analysis.
Seamless Integration: Easily integrates with SAP environments, enhancing data accessibility.

What benefits should users consider in reviews?

Informed Decision-Making: Provides insights that help businesses make data-informed decisions.
Efficiency in Analysis: Reduces the time needed to develop and deploy predictive models.
Scalability: Supports expanding data needs as businesses grow.

In industries like manufacturing and retail, SAP Predictive Analytics [EOL] helped optimize supply chains and inventory management by forecasting demand trends. Financial sector users implemented it to enhance risk analysis and fraud detection models, providing valuable insights for mitigating potential risks.

SAP

Sample Customers

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

mBank

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