Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
The primary obstacle in expanding AI-driven decision-making lies in the underutilization of data. IBM Cloud Pak® for Data provides a cohesive platform that integrates a data fabric, enabling seamless connection and access to isolated data, whether it resides on-premises or in various cloud environments, without necessitating data relocation. It streamlines data accessibility by automatically identifying and organizing data to present actionable knowledge assets to users, while simultaneously implementing automated policy enforcement to ensure secure usage. To further enhance the speed of insights, this platform incorporates a modern cloud data warehouse that works in harmony with existing systems. It universally enforces data privacy and usage policies across all datasets, ensuring compliance is maintained. By leveraging a high-performance cloud data warehouse, organizations can obtain insights more rapidly. Additionally, the platform empowers data scientists, developers, and analysts with a comprehensive interface to construct, deploy, and manage reliable AI models across any cloud infrastructure. Moreover, enhance your analytics capabilities with Netezza, a robust data warehouse designed for high performance and efficiency. This comprehensive approach not only accelerates decision-making but also fosters innovation across various sectors.
Description
Rapidminer SLC is a Siemens software solution built to help organizations modernize analytics environments while continuing to use existing SAS language programs. It gives analysts, developers, and operations teams a flexible way to work with SAS language, Python, R, SQL, no-code tools, and drag-and-drop workflows in one ecosystem. The platform helps reduce migration risks by maintaining functionality for current SAS language applications while enabling gradual adoption of open-source analytics. Rapidminer SLC supports on-premises, cloud, and hybrid deployments, giving organizations more freedom to evolve infrastructure without disrupting business operations. Users can connect to a wide range of data sources, including cloud services, Hadoop, data warehouses, databases, Microsoft Excel, CSV files, SPSS, SAS language formats, and other file-based data. Its modern IDE allows teams to create, maintain, run, and analyze programs while reviewing data, results, and logs in one environment. Rapidminer SLC also makes it possible to exchange data between SAS language, Python, R, and SQL for more connected analytics development. Rapidminer SLC Hub adds enterprise management features for security, load balancing, publishing, deployment, and workload allocation. By combining legacy analytics support with modern open-source flexibility, Rapidminer SLC helps organizations improve productivity, scalability, and long-term analytics innovation.
API Access
Has API
No
API Access
Has API
No
Integrations
Datamatics Trade Finance
Yes
IBM Control Desk
Yes
IBM Db2 Big SQL
Yes
IBM Db2 Event Store
Yes
IBM InfoSphere Information Governance Catalog
Yes
IBM Netezza Performance Server
Yes
IBM Storage Software Suite
Yes
IBM WebSphere Commerce
Yes
Rapidminer
No
Rapidminer Knowledge Studio
No
Integrations
Datamatics Trade Finance
No
IBM Control Desk
No
IBM Db2 Big SQL
No
IBM Db2 Event Store
No
IBM InfoSphere Information Governance Catalog
No
IBM Netezza Performance Server
No
IBM Storage Software Suite
No
IBM WebSphere Commerce
No
Rapidminer
Yes
Rapidminer Knowledge Studio
Yes
Pricing Details
$699 per month
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
IBM
Founded
1911
Country
United States
Website
www.ibm.com/products/cloud-pak-for-data
Vendor Details
Company Name
Siemens
Founded
1847
Country
Germany
Website
www.siemens.com/en-us/products/rapidminer/slc/
Product Features
Big Data
Collaboration
No
Data Blends
No
Data Cleansing
No
Data Mining
No
Data Visualization
No
Data Warehousing
No
High Volume Processing
No
No-Code Sandbox
No
Predictive Analytics
No
Templates
No
Data Fabric
Data Access Management
No
Data Analytics
No
Data Collaboration
No
Data Lineage Tools
No
Data Networking / Connecting
No
Metadata Functionality
No
No Data Redundancy
No
Persistent Data Management
No
Data Science
Access Control
No
Advanced Modeling
No
Audit Logs
No
Data Discovery
No
Data Ingestion
No
Data Preparation
No
Data Visualization
No
Model Deployment
No
Reports
No
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No
Predictive Analytics
AI / Machine Learning
No
Benchmarking
No
Data Blending
No
Data Mining
No
Demand Forecasting
No
For Education
No
For Healthcare
No
Modeling & Simulation
No
Sentiment Analysis
No
Product Features
Data Analysis
Data Discovery
No
Data Visualization
No
High Volume Processing
No
Predictive Analytics
No
Regression Analysis
No
Sentiment Analysis
No
Statistical Modeling
No
Text Analytics
No
Data Mining
Data Extraction
No
Data Visualization
No
Fraud Detection
No
Linked Data Management
No
Machine Learning
No
Predictive Modeling
No
Semantic Search
No
Statistical Analysis
No
Text Mining
No
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No
Predictive Analytics
AI / Machine Learning
No
Benchmarking
No
Data Blending
No
Data Mining
No
Demand Forecasting
No
For Education
No
For Healthcare
No
Modeling & Simulation
No
Sentiment Analysis
No