As a workflow-based analysis software, KNIME allows users to go through a series of analysis processes such as Import/Pre-processing/Transformation/Analysis/
Evaluation/Visualization from various data sources. In other words, it is a total analysis solution.
Intuitive Data Analysis
based on Workflows
Support for Various
Sources/Languages
Structured/Unstructured
Data Analysis
Gartner's 'Magic Quadrant' for
Data Science/Machine Learning Platforms
Features
KNIME is intuitively designed so that anyone, including developers, data engineers, and data scientists, can easily understand data, design data analysis workflows. Automating analysis elements is another huge plus for convenient use.
Provide Open Source and
Commercial Server Source
Free of charge for desktop,
diverse pricing plans for server use
Intuitive Analysis
Workflow-based management
of the analysis process at a glance
Multi-faceted Accessibility
DB/Hadoop table, Hive file, formatted file
Support Various Languages
R, Python, Java
Data Utilization
Analysis of structured/unstructured data
Easy Use
Drag & Drop method
Various Data Pre-processing/Analysis
About 4,600 nodes
Automated Analysis
Schedule registration for workflow
Highlights
End-to-End Analysis
KNIME enables users to access various data sources such as DB tables, Hadoop files, CSV, and Excel, and analyze both structured and unstructured data. With over 4,600 nodes available on the desktop, it provides a comprehensive environment where users can perform data import, analysis, modeling, and visualization.
Workflow Designer
KNIME is a VPL (Visual Programming Language) based solution, so with a simple drag and drop, users can create workflow without additional coding process. KNIME Server enables team-based collaboration and automated workflow, as well as access to complex data types and adding machine learning and deep learning algorithms.
Enterprise Software
KNIME is an easy-to-use tool that even non-experts can operate. Users can access analysis results through the KNIME web portal. Moreover, KNIME offers user-friendly features such as team-based collaboration, automation, management, and deployment. Additionally, it can be integrated with analytics application services and real-time IoT systems using REST APIs.
Architecture
Customer Cases
Energy
Development of 4 Core Analysis
Technique Cases
Adoption Background
As the importance of data analysis increases significantly, there is a demand for a solution that anyone, not just data experts, can easily analyze and utilize big data.
Benefits
• Development of analysis cases for 4 core analysis techniques (cluster analysis, regression analysis, decision tree, artificial neural network) using KNIME.
• Obtaining real-time power data analysis results for cooling/heating, other loads, base load temperature, and daily/weekly load through real-time power consumption and prediction analysis.
• All information is posted on the portal within the pre-built big data integration platform, and a knowledge sharing space is prepared to refer to similar cases and learn analysis methods during analysis.
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