KNIME Analytics Platform Reviews

27 Ratings
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Score 8.0 out of 101

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Reviews (1-6 of 6)

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Score 8 out of 10
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KNIME is used across the entire business. We primarily use it as an ETL tool and to act as the input for other BI software such as Tableau and QlikView. KNIME is also used for initial machine learning functions.
  • KNIME works better than most tools for ETL functions.
  • Easy to track the different steps
  • Easy to isolate and fix specific workflow steps.
  • It does not have proper visualization.
  • Some other BI tools (QlikView) have much easier functions for data interaction.
  • Some other BI tools (Tableau) can be set up much faster.
  • It is not an easy tool to use for non-tech savvy staff.
KNIME is quite useful for initial data exploration and to share and discuss your process (workflow) with someone that does not how KNIME works. KNIME's visualisation tools can not be compared to most BI tools, because of the limited amount of available charts.
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Rohit Narang profile photo
Score 9 out of 10
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We use KNIME due to its high-value predictive analytics and its ability to find patterns as a data mining tool. Its risk analytics are used in our department for the development of new models and model validation using time series for low default portfolios. Primarily for creating univariate and multivariate analysis and finding statistical significance of variables, and further correlations with a blend of statistical procedures in the banking industry.
  • For non-programming based functional users, it's a blessing as it doesn't require coding, programming skills to perform data mining. The full desktop version of KNIME is free and open source, with no limit to data.
  • Connect to Open source: It also offers excellent integration with a wide range of other open source software such as Python, R, Spark, and even ImageJ for image analysis.
  • Great Integration of functionalities: We never move data between applications/platforms to complete the project. Raw data is easily ingested in the tool, processed, can be performed statistics, summarised and exported to various formats.
  • Visualization can be improved further though it has been better with new versions, with a lot of scope available. However, connectivity to Tableau somehow overcomes this. Also, skilled resources are difficult to find for KNIME, due to other solutions having better penetration.
  • Knowledge of R/Python is required to fully use the statistical analysis (rather than just data mining). Also, memory usage is a problematic issue sometimes.
  • Not enough domain usage experience can be shared between KNIME users as well.
It is well suited for organizations having day to day advanced statistical procedures requirements. We use ANOVA, multivariate regression using time series modeling and several calibrations in our models for periodic change due to agile macro-sensitive economic forecasts.
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Viktor Mulac profile photo
Score 9 out of 10
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I have used KNIME for advanced data analytics and experiments in the AI (machine learning) area. I have also used this platform for running client data analysis in sourcing and sales areas, including running of prediction models.
This is a framework that allows you to start with simple tasks and gradually increase the analysis complexity.
After going repeatedly through several data sources with tons of data, the painful part has always been preparing and transforming the raw data for analysis. This can be automated and the data acquisition model can be saved and run repeatedly, saving a lot of time. Data cleansing and blending of tables is easy here. It also supports formats as JSON, XML, a quite frequent format nowadays.

Above all the platform and community is wide with hundreds of add-on modules. Frequently, someone has already solved a similar task as you. Before trying to model anything from scratch, it is a good idea to skim through modules and hopefully you can find a good one to use. And finally, it supports simple as well as complex analytics, including AI algorithms.
  • Great UX interface, easy connection of data sources, good handling of the analytical model, easy to modify.
  • It provides good level of control of what happens with your data in each step.
  • Great tool from data preprocessing, from analysis to visualization.
  • Great community and a lot of modules to reuse.
  • Supports machine learning - it is easy to configure and run.
  • It is Open Source!
  • If you are familiar with Python, you can use this easy programming language to add additional functions to your analytical model.
  • Automation - e.g. RapidMiner Studio provides a Turbo Prep function, where one can get to working on models more quickly (RapidMiner is not open source though)
  • KNIME does not replace a regular reporting tool - it is not meant to. However, if I have already spent some time developing a data acquisition and analytical model, it would be nice to be able to deploy, for example, a monitoring or reporting module that would process data autonomously and react accordingly.
If you are searching for a tool with a low total cost of ownership (TCO), that is easy to understand, and that comprises many prepared modules, KNIME is great. The tool is very intuitive with a lot of examples to learn. You can find a bit better tools, like RapidMiner Studio, but this is a paid, commercial solution. Yes, you can get a free RapidMiner license to process up to 50,000 lines of data, but this is not sufficient for serious work. Most of my use cases today require a bigger license, so KNIME is an attractive alternative price-wise.
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Score 8 out of 10
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KNIME has been used as a training tool for students to use. This program acts as a basic way for students with limited bioinformatic and computational skills to solve big data problems. The program has been used for simulated drug discovery training purposes.
  • Easy to use
  • Open source; extra programs can be added easily
  • User interface can be crowded at times
KNIME Analytics Platform is well suited as a training program for students from a variety of computation backgrounds. It integrates well many of the common chemical and biological programs and data files into one program that can then be used to process and sort large inventories.
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Sergio Pulido Tamayo profile photo
Score 10 out of 10
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Verified User
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We use KNIME across the whole organization. It is used to solve a wide range of business problems, from ETL and data integration, to advanced analytics and customer segmentation.
  • Connect to different data sources (uses JDBC)
  • Process large quantities of data
  • Integrate different machine learning frameworks and techniques
  • Use and integrate with cloud and big data environments
  • Does not have integration with Jupyter Notebooks
  • The tools for script writing and development are not easy to use or don't have many features
  • Memory usage is problematic some of the time
Perfect for training of non-expert users. It is well suited for any kind of analytics endeavor. It is appropriate for many information automation tasks.
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Score 10 out of 10
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We use KNIME Analytics for our client (one of the Big Four). The use of this platform is for NLP related tasks. Specifically for Information Retrieval. It is used by a division within the organisation.
  • Text processing is easily performed by the various extensions within this platform
  • Integrates multiple languages like Python, R , Java etc. all in one place
  • Also provides many options for text parsing like CoreNLP, OpenNLP
  • Documentation is poor
  • The developers are mostly not native English speakers therefore their verbiage is sometimes ambiguous in the given examples
It is good for data ingestion given various formats. Thereafter more time can be dedicated to data analysis and other downstream tasks.
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Feature Scorecard Summary

Connect to Multiple Data Sources (3)
8.7
Extend Existing Data Sources (3)
7.3
Automatic Data Format Detection (3)
8.7
MDM Integration (3)
6.0
Visualization (3)
5.6
Interactive Data Analysis (3)
5.9
Interactive Data Cleaning and Enrichment (3)
7.0
Data Transformations (3)
7.0
Data Encryption (3)
6.7
Built-in Processors (3)
8.0
Multiple Model Development Languages and Tools (2)
7.5
Automated Machine Learning (2)
7.0
Single platform for multiple model development (2)
6.5
Self-Service Model Delivery (2)
5.5
Flexible Model Publishing Options (1)
8
Security, Governance, and Cost Controls (1)
6

About KNIME Analytics Platform

Swiss company KNIME offers their KNIME Analytics Platform for big data and predictive analytics.

KNIME Analytics Platform Technical Details

Operating Systems: Unspecified
Mobile Application:No