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
Top Performing Features
Extend Existing Data Sources
Use R or Python to create custom connectors for any APIs or databases
Cat avg: 8.9
Interactive Data Cleaning and Enrichment
Access to visual processors for data wrangling
Cat avg: 9
Data Transformations
Use visual tools for standard transformations
Cat avg: 9.1
Worst Performing Features
Visualization
The product’s support and tooling for analysis and visualization of data.
Cat avg: 8.3
MDM Integration
Integration with MDM and metadata dictionaries
Cat avg: 7.8
Self-Service Model Delivery
Multiple model delivery modes to comply with existing workflows
Cat avg: 8.3
Jupyter Notebook Features from Reviews
Platform Connectivity
Ability to connect to a wide variety of data sources
9.0+2%
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
7-19%
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
9.5+6%
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
9.3+3%
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
10+14%
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