KNIME Analytics Platform

KNIME Analytics Platform is an open-source analytics solution for data scientists created by KNIME AG. It is an enterprise-grade platform that boasts an easy-to-use, straightforward, and intuitive interface. It uses the power of the Eclipse platform and a set of other powerful extensions designed for machine learning and data mining to facilitate users in discovering potential hidden data, predict new rules and fresh information. If you are an analytically-minded expert, then the KNIME Analytics Platform is a solution you should go for. This software has over 2000 modules, a wide spectrum of integrated tools, advanced algorithms, and ready-to-deploy examples. This program offers an intuitive graphical interface for easy node assembly that connects different data sources. Some major characters included are workflow difference, big data extensions, meta node linking, robust analytics, and data blending.

Features of KNIME Analytics Platform

So here some important features of the KNIME Analytics Platform as follows:

Big Data Extensions- KNIME Big Data Extensions integrate the power of Apache Hadoop and Apache Spark along with the KNIME Analytics Platform and KNIME Server. KNIME software takes the confusion out of big data by making it accessible within the familiar analytics environment.

Data Blending- Data Blending in KNIME includes simple text files, databases, documents, images, networks, and even Hadoop-based data that can all be combined within the same visual workflow.

Tool blending- Tool blending means integration of multiple tools like legacy scripting/code, allows expertise to be reused, graphically documented, and shared among data scientists.

Meta node linking- Meta nodes are gray nodes that comprise of sub-workflows. They are used as functions or macros in the script-based tools.

Local automation- Local Automation allows the call workflow nodes to be involved in any workflow. This allows the formation of reusable workflows, adding another layer of flexibility to your toolkit.

Workflow difference- Manual identification and comparison of workflows can be time-consuming and difficult. Workflow Difference automates this task by applying a matching algorithm to analyze and identify additions, removals, replacements, and limitation changes among nodes and workflows.

Data Manipulation- It is another frequently used module. It can assume and predict your data with the ability to filter, group, pivot, bin, normalize, aggregate, join, sample, partition, and so on.

Data Mining- It makes use of multiple algorithms like clustering and neural networks to help the KNIME user better understand their data.

Advantages of KNIME

The main advantages of the KNIME Analytics Platform are powerful analytics, local automation, and workflow difference. Read on to understand further:

Scalable Platform:
KNIME Analytics Platform is a scalable data analytics platform that enables data scientists to toggle effortlessly on come computer, stream, and execute big data. It offers statistical and mathematic functions, workflow controls, advanced predictive algorithms, machine learning algorithms, and much more to help you streamline your tasks. Its user-friendly interface is outlined to help you easily navigate the software and assist in creating data flows, implementing specific analysis tactics, and analyzing results, models, and interactive reviews easily.

Powerful & Robust Plugins:
KNIME Analytics Platform is created using the power of Eclipse platforms and uses the ability of its module extension through plugins and connectors. Its robust plugins are created to support the integration using state-of-the-art techniques for text and image mining and time series analysis. With the blocking platform, this software has over 2,000 modules that support different data connectors for various file formats and major databases. These modules are also supporting machine learning algorithms, different data types (such as images, documents, XML, and JSON), and multiple statistical works.

Supports Deep Learning:
KNIME Analytics is also connected with different open-source projects such as H2O, ScikitLearn, and Keras machine learning algorithms. These integrations support deep learning and wrappers for calling codes. It also supplies nodes to help users run Python, Java, Perl, and other coding scripts. It supports multiple web-based reporting techniques and offers a complete design of workflow.

Automation:
The local automation capability of this software offers an added layer of flexibility to your arsenal while allowing you to make a reusable workflow. Its workflow difference functionality allows you to track all modifications made on your workflow, helping you compare changes made by your colleagues, and ensuring security against unintentional changes.

Disadvantages of KNIME

Due to penetration in other programming languages, skilled resources are hard and difficult to find for KNIME. However, connectivity to Tableau somehow overcomes this. Understanding and visualization can be improved further though it has been better with new versions, with a lot of scopes available.

Understanding and knowledge of R/Python are required to fully use the statistical analysis (rather than just data mining). Also, memory usage is a problematic issue sometimes.

The tools used for scriptwriting and developments are not easy to use or don’t have many features.

Speed: It works slowly, especially the opening.

 

Conclusion:

If You have any queries you can drop your questions below,
we will be happy to solve your problems.

Thanks for reading…!!!
Pattanayak Engineering