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Description
IBM Lift simplifies the process of migrating your database from on-premises facilities to an IBM Cloud® data environment by ensuring a quick, safe, and dependable transition. This tool is specifically crafted for seamless cloud migration without any downtime, allowing you to transfer your entire database to the IBM Cloud effortlessly. The migration process consists of two main steps: schema conversion and data migration. To initiate schema conversion, you will need to download the IBM Database Conversion Workbench, which guides you through adapting your source database DDL for compatibility with your target system. Additionally, the workbench generates a report highlighting specific areas that require your attention. After successfully setting up your schema, you can proceed to migrate your data using the Lift CLI. Begin by creating a series of CSV files that capture your incremental changes for each database table. Then, utilize the Lift CLI to collect these delimited files, transmit them securely, and import them into IBM® Db2® Warehouse on Cloud, ensuring a smooth and efficient migration experience. By following these steps, you can ensure a successful transition to the cloud with minimal disruption.
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
Rapidminer
No
Rapidminer Knowledge Studio
No
Rapidminer Monarch
No
Rapidminer Panopticon
No
Integrations
Rapidminer
Yes
Rapidminer Knowledge Studio
Yes
Rapidminer Monarch
Yes
Rapidminer Panopticon
Yes
Pricing Details
No price information available.
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
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
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/cloud/lift
Vendor Details
Company Name
Siemens
Founded
1847
Country
Germany
Website
www.siemens.com/en-us/products/rapidminer/slc/
Product Features
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
