KNIME Reviews

In demand forecasting projects to extract, to clean and to transform data from various resources. Also some clustering and classification techniques are used for behavioural clustering and classification according to attributes.

My organization's field of activity is to develop business applications for niche areas. Almost three years ago, we decided to extend our solutions with advanced analytics. KNIME let us start easy and fast into the Advanced Analytics area. We are able to try project ideas with KNIME by doing proof of concept easy and prototyping fast.

• Easy ETL operations
• Rich algorithm set
• Integrated with other languages like R, Python, and Java.
• Works together with other technologies like DeepLearning4j, H2O.ai, D3.js, and Weka.
• Ease of use and being a performant solution.
• Continues development and wide community support

I mentioned about the distributed architecture in my previous answer, but they did with version 3.5. This time maybe I could add the integration with graph databases like Neo4j.

In the previous versions, I had some issues when reading large Excel files due to memory usage. But with the previous version (3.3), they renewed all Excel nodes and now I have no problem. 

With the data sizes that I dealt with, I did not. Our average record size was around 10 million records. If we have bigger data, we can opt for a Big Data extension for Hadoop, Spark, etc.

I used it very little. All of them replied to me in one day. (It was not professional support, just over a forum). Also, I can find enough information in the documentation and forum.

Before KNIME, I used SQL language and Excel for data analysis but machine learning algorithms. In parallel to KNIME, I worked on a few projects with R and Python separately. So I cannot say that I switched from different solutions.

But just for ETL with Excel, KNIME brings me better visualization, rich function set, preserving operations to repeat again and better performance on the same hardware.

I am using Mac and it is so easy. Download a .dmg, extract it as an app, and copy it to the applications folder. On windows it is also simple installation.

For extensions like R or Python, you need experience with general OS and installation processes.

We did in-house.

The biggest ROI comes from productivity when creating new things and also supporting old jobs.

And there is no hidden cost. Licensing is simple and open than other platforms.

KNIME is open sourced platform and has a free desktop version with unlimited data size and functionality.

Also, the server version is good for teams and enterprise productivity. Especially the new "Model Factory", which lets data science teams easily build and manage models. When compared with similar products, it is less expensive but as powerful as (or maybe more powerful than) others.

The Open Source licensing and community support is one of our important criteria. The second one is the interoperability with other technologies and openness to different data sources. There are two options: RapidMiner and KNIME. We chose KNIME.

Data Science requires freedom for creativity. Sometimes you need to crawl data from the web or social media. Sometimes you need to blend different sources like NoSQL MongoDB and Excel files, etc. It is not only algorithms and data extraction, visualization and preparation steps are important as at least algorithms.

Don't go with software that has complex and hidden licensing costs, which will kill your flexibilty and creativity. Also, interoperability brings the advantage of limitlessness.

Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing.

Lot of manual work has been automated through this and single solution created on this can be used by others, too. We have implemented the same thing in our organization as CAE.

Data handling capacity. Currently it handless 25M rows of data, after which you will face lagging issue.

System resource usage. Knime will occupy total system RAM size and other applications will hang.

I have been using it for 18 months.

Nope.

When the data limit exceeds, it will get lag.

A nine out of 10.

Previously, we used Alteryx for harmonizing. Cost was the main reason for the switch.

It is easy to setup.

It is free of cost. It is GNU licensed.

No.

It is best one for harmonizing data with no cost included.

• It has an excellent interface
• Is very easy to use
• It really boosts productivity
• The performance is impressive, even in a normal laptop
• The diversity of tools for pre-processing is very handy
• The integration with R and Weka, make it the perfect Data Mining solution

We have use it in industry projects, for internal experiments, and for teaching.

The diversity of native algorithms could be improved.

I have been using this solution for the past three years and it is an excellent solution.

No issues with deployment.

No issues with stability.

No issues with scalability.

Once I had to contact the customer service, and it worked quite well.

Knime has a considerable support community, so there are few problems that you can not find in the documents of the community.

I used the old Clementine solution (now in the IBM portfolio). I also keep using Weka, but now I tend to use Knime more, which ends up being more versatile, since it can integrate R and Weka.

The setup is straightforward, just download and start using.

I implemented in-house and for other companies.

Very high, since it has low costs and it is very easy to use.

Yes, but long ago. I evaluated Oracle Data Mining, Clementine, and SAS Enterprise Miner.

At our company, we provide consultation services to federal institutions, and in some cases, we recommend KNIME to clients and have also implemented the solution for a few projects. 

The graphic features of KNIME need improvement, especially when working on dashboards. 

I have been using KNIME for three years. 

The stability of KNIME depends on the resources on the computer in which it's installed. KNIME requires a high capacity of RAM and memory; if the hardware needs are fulfilled, then the product shows satisfying stability. 

When I used to work on KNIME every day, I faced some issues, but they were quickly resolved with the help I received from the active KNIME community. 

Our organization has about ten KNIME users, and the solution is used around once a month. 

I would rate tech support a nine out of ten. 

Positive

I have experience in Qlik Sense, Tableau, and native Python programming. I personally switched to KNIME because my present employer had KNIME already implemented in the company. 

The initial setup of the solution is easy. Installing the KNIME Analytics free version on a desktop takes only a few minutes. The setup and installation process is quite easy to begin. 

The implementation was carried out in-house. The solution is free to use; therefore, a partner or reseller is not needed. For the KNIME Business Hub, our organization is directly communicating with KNIME, and a reseller isn't required. 

KNIME offers a free version to use. KNIME also has a server version with almost the same price as competitors. 

In data analytics workflows at our company, we use KNIME for the preprocessing of data, and for a few projects, an ETL track was built using KNIME. We are planning to use KNIME Business Hub to create data apps where a dashboard of analysis can be prepared and presented to others. 

Our company mostly uses KNIME for small datasets, projects, and customers who don't have much expertise in data analysis. We introduce KNIME to our organization's customers to get started with data analysis. The solution is very easy to use. For advanced data analytics, I use a tool different from KNIME. 

The product has impressive data-blending capabilities. The solution is user-friendly and helps our company easily onboard new users. I would definitely recommend the product to others, majorly to individuals with beginner-level programming and data analytics skills. I would rate KNIME an eight out of ten.