Extend Existing Data Sources
Use R or Python to create custom connectors for any APIs or databases
Cat avg: 8.9
Top Performing Features
Connect to Multiple Data Sources
Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion
Cat avg: 8.8
Data Transformations
Use visual tools for standard transformations
Cat avg: 9.1
Multiple Model Development Languages and Tools
Access to multiple popular languages, tools, and packages such as R, Python, SAS, Jupyter, RStudio, etc.
Cat avg: 9.2
Worst Performing Features
Self-Service Model Delivery
Multiple model delivery modes to comply with existing workflows
Cat avg: 8.3
Security, Governance, and Cost Controls
Built-in controls to mitigate compliance and audit risk with user activity tracking
Cat avg: 8.6
Data Encryption
Data encryption to ensure data privacy
Cat avg: 8.4
KNIME Analytics Platform Features from Reviews
Platform Connectivity
Ability to connect to a wide variety of data sources
9.2+4%
Connect to Multiple Data Sources
Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion
Category average: 8.8
Extend Existing Data Sources
Use R or Python to create custom connectors for any APIs or databases
Category average: 8.9
Automatic Data Format Detection
Automatic detection of data formats and schemas
Category average: 9.2
MDM Integration
Integration with MDM and metadata dictionaries
Category average: 7.8
Data Exploration
Ability to explore data and develop insights
8.1-6%
Visualization
The product’s support and tooling for analysis and visualization of data.
Category average: 8.3
Interactive Data Analysis
Ability to analyze data interactively using Python or R Notebooks
Category average: 8.8
Data Preparation
Ability to prepare data for analysis
8.3-7%
Interactive Data Cleaning and Enrichment
Access to visual processors for data wrangling
Category average: 9
Data Transformations
Use visual tools for standard transformations
Category average: 9.1
Data Encryption
Data encryption to ensure data privacy
Category average: 8.4
Built-in Processors
Library of processors for data quality checks
Category average: 9
Platform Data Modeling
Building predictive data models
8.0-11%
Multiple Model Development Languages and Tools
Access to multiple popular languages, tools, and packages such as R, Python, SAS, Jupyter, RStudio, etc.
Category average: 9.2
Automated Machine Learning
Tools to help automate algorithm development
Category average: 8.9
Single platform for multiple model development
Single place to build, validate, deliver, and monitor many different models
Category average: 9.5
Self-Service Model Delivery
Multiple model delivery modes to comply with existing workflows
Category average: 8.3
Model Deployment
Tools for deploying models into production
7.3-17%
Flexible Model Publishing Options
Publish models as REST APIs, hosted interactive web apps or as scheduled jobs for generating reports or running ETL tasks.
Category average: 9.2
Security, Governance, and Cost Controls
Built-in controls to mitigate compliance and audit risk with user activity tracking
Category average: 8.6
KNIME Analytics Platform Features from the Vendor
Platform Connectivity
Vendor-contributedConnect to Multiple Data Sources
Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion
Extend Existing Data Sources
Use R or Python to create custom connectors for any APIs or databases
Automatic Data Format Detection
Automatic detection of data formats and schemas
Data Exploration
Vendor-contributedVisualization
The product’s support and tooling for analysis and visualization of data.
Interactive Data Analysis
Ability to analyze data interactively using Python or R Notebooks
Data Preparation
Vendor-contributedInteractive Data Cleaning and Enrichment
Access to visual processors for data wrangling
Data Transformations
Use visual tools for standard transformations
Platform Data Modeling
Vendor-contributedMultiple Model Development Languages and Tools
Access to multiple popular languages, tools, and packages such as R, Python, SAS, Jupyter, RStudio, etc.
Automated Machine Learning
Tools to help automate algorithm development
Single platform for multiple model development
Single place to build, validate, deliver, and monitor many different models
Self-Service Model Delivery
Multiple model delivery modes to comply with existing workflows
Model Deployment
Vendor-contributedFlexible Model Publishing Options
Publish models as REST APIs, hosted interactive web apps or as scheduled jobs for generating reports or running ETL tasks.
Security, Governance, and Cost Controls
Built-in controls to mitigate compliance and audit risk with user activity tracking