Knime Analytics Platform: A dream for a data scientist

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In this blog, we are going to see, what is the Knime analytics platform and its important features to create an analytics workflow in an easy way.

Introduction to Knime Analytics Platform

KNIME is a platform built for powerful analytics on a GUI based workflow. This means you do not have to know how to code to be able to work using KNIME and derive insights. 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 fact that there’s neither a paywall nor locked features means the barrier to entry is nonexistent. The platform is totally open sourced and free, you didn’t have to pay anything for any locked feature.

You can go through the document at and there are a ton of free extensions to the platform.


whenever someone starts with data science or machine learning, the main problem is, you have a lot of things to learn. For example, learning about mathematical concepts, Statistics, and after this, you should know how to code them. Sometimes, it got frustrated for new users.

How about only focus on the learning part and leave the coding part initially?.

The solution is you can start learning data science with a tool that is GUI driven. Knime Analytics Platform is one of the most promising and trusted GUI driven tool to create analytic workflows.

By doing so, you can learn about basic concepts first and then begin with the coding.

What can I do with the KNIME Analytics Platform?

KNIME Analytics Platform is well-suited for the following:

  • ETL processes (moving data around from here to there and cleaning it up)
  • Machine learning
  • Deep learning
  • Natural language processing
  • API integration
  • Interactive visual analytics (somewhat of a beta feature)

Important terms

When you will start your journey with knime or start learning it, you with hear Node and Workflow frequently. so before you get started with knime, it’s better to understand these terms:


source: Knime doc

A node is the basic processing point of any data manipulations. Each node is displayed as a colored box with input and output ports. The input(s) is the data that the node processes and the output(s) are the resulting datasets. Each node has a status indicator right below the node.

  • Not configured: whenever we import the node in our workflow, the node indicator indicates the red light. which means the node is not configured.
  • Configured: when we configured the node, the indicator will switch from red light to yellow. It means the node is ready to be executed.
  • Executed: Now if our node is configured, we need to execute the node to perform defined action. After execution, the indicator will turn to green light.
  • Error: If you will find the cross red symbol on your node indicator, it means there is an error in your execution.


A workflow is the sequence of steps or actions you take in your platform to accomplish a particular task. You can see in the above figure, a combination of nodes combined in a sequence created a nice workflow.

Note: Nodes can perform all sorts of tasks, including reading/writing files, transforming data, training models, creating visualizations, and so on.

If you want to get started and learn how to create the first data science workflow, watch our last webinar here:


Stay Tunes, happy learning 🙂

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Written by 

Shubham Goyal is a Data Scientist at Knoldus Inc. With this, he is an artificial intelligence researcher, interested in doing research on different domain problems and a regular contributor to society through blogs and webinars in machine learning and artificial intelligence. He had also written a few research papers on machine learning. Moreover, a conference speaker and an official author at Towards Data Science.