KNIME Valuable Features

AmanKumar
Business Intelligence Consultant at a tech services company with 1,001-5,000 employees
The most valuable part of the solution is the machine learning part. The second feature that we use most is big data connectivity. When we deployed the architecture, we directed our IDS (Intrusion Detection System) server to where the big data will be on our servers. Then we needed some kind of basic machine learning and obviously. After that, we connected it with Tableau visualization. Now we are writing the big data part of our solution along with the overall machine learning. These two parts will be the most important for our business going forward. I think also connectivity with hybrid databases and also integration with languages like Python are great advantages to what we are seeking to do in our environment. We have been using these features extensively and we find them to be very valuable in achieving what we hoped to achieve with the tool. View full review »
Agus Kurdiyanto
Solution Integrator at Ericsson
Most important, it is open-source. Next is the ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database. View full review »
Dusty Evely
Business Analyst at a tech services company with 201-500 employees
I know I don't use it to its full capacity, but I love the Rule Engine feature. It has allowed me to create lookup tables on the fly and break down text fields into quantifiable data. View full review »
Find out what your peers are saying about Knime, Weka, IBM and others in Data Mining. Updated: February 2020.
398,890 professionals have used our research since 2012.
Giovanni Marano
Senior Data Scientist
* Easy to connect with every database: We use queries from SQL, Redshift, Oracle. * Easy to have a clear view of the data at every single step of the ETL process, with the consequent possibility of changing the flow according to your needs. View full review »
Hilton Rossenrode
Business Analyst at a retailer with 501-1,000 employees
* Visual workflow creation * Workflow variables (parameterisation) * Automatic caching of all intermediate data sets in the workflow * Scheduling with the server View full review »
Wim Michielsen
Data Science Consultant
* The very easy-to-use visual interface * Help functions and clear explanations of the functionalities and the used algorithms * Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis For inexperienced analysts or data scientists, it is a very easy tool to take your first steps in modeling and analytics. View full review »
Ling Li
Intern at a energy/utilities company with 10,001+ employees
It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop. View full review »
Evans Otalor
Business Intelligence Manager at Telecoms
Easy to use nodes for ETL processes. This is because, in many cases, I usually transform the data before the main task even when the data is from a structured database. View full review »
Prithviraj Dutta
Data Scientist at a tech services company with 1,001-5,000 employees
The most useful features are the readily available extensions that speed up the work. For instance, KNIME offers multiple document taggers, which one can use with relative ease. Similarly, the number of predefined NER taggers are also very handy. View full review »
Angus Lou
Head Of Business Solutions | Unmanned Shop | Automated Retail | AI | IoT | Robotic | Data Science with 51-200 employees
This solution is easy to use and especially good at data preparation and wrapping. It is useful for making a data pipeline to automate data processing tasks. The most valuable feature is to automate what is manually processed. View full review »
Find out what your peers are saying about Knime, Weka, IBM and others in Data Mining. Updated: February 2020.
398,890 professionals have used our research since 2012.