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What is Rapidminer SLC?

Rapidminer SLC is a Siemens analytics platform designed to help organizations modernize SAS language environments while expanding into open-source and multi-platform analytics. It allows teams to keep using existing SAS language programs while also working with Python, R, SQL, no-code tools, and drag-and-drop workflows. The solution helps organizations reduce migration risk by supporting existing analytics assets rather than requiring a disruptive replacement process. Rapidminer SLC is built to maintain business continuity while giving analysts, developers, and operations teams a path toward a more flexible analytics ecosystem. Users can create, execute, and operationalize analytics in modern environments across on-premises, cloud, and hybrid infrastructures. The platform supports access to many data sources, including cloud services, Hadoop, data warehouses, databases, Microsoft Excel, CSV, SPSS, SAS language data, and other file-based formats. Its Analytics Workbench provides a modern IDE for building, maintaining, running, and reviewing programs across multiple languages. Teams can explore data, results, and logs using code, no-code methods, or a combination of both. Rapidminer SLC makes it easier to exchange data between SAS language, Python, R, and SQL, helping organizations blend established workflows with newer analytics approaches. Rapidminer SLC Hub supports enterprise needs such as security, load balancing, deployment, publishing, scheduling, user provisioning, and workload distribution across resilient node clusters. With broad data access, multi-language development, centralized governance, and enterprise execution tools, Rapidminer SLC helps businesses transition analytics infrastructure while protecting existing investments.

What is Oracle Data Miner?

Oracle Data Miner enables data scientists, "citizen data scientists," and both business and data analysts to engage with data directly within the database using a user-friendly graphical interface that features a "drag and drop" workflow editor. As an enhancement to Oracle SQL Developer, Oracle Data Miner (ODMr) adeptly captures and visually presents the analytical steps that users undertake while exploring data and developing machine learning models. The workflows established using ODMr are crucial for not only reapplying analytical techniques but also for fostering knowledge exchange among team members. Additionally, ODMr generates SQL and PL/SQL scripts seamlessly and offers a workflow API that aids in the efficient deployment of models throughout the organization. This capability significantly reduces data movement, ensures scalability for large datasets, upholds security, and accelerates the transition from model creation to implementation. By adopting this advanced methodology, organizations can better leverage their data resources, ultimately resulting in smarter decision-making and enhanced business performance. Furthermore, this systematic approach helps to create a culture of data-driven insights that can transform strategic initiatives.

Media

Media

Integrations Supported

Rapidminer
Rapidminer Knowledge Studio
Rapidminer Monarch
Rapidminer Panopticon

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Siemens

Date Founded

1847

Company Location

Germany

Company Website

www.siemens.com/en-us/products/rapidminer/slc/

Company Facts

Organization Name

Oracle

Date Founded

1977

Company Location

United States

Company Website

www.oracle.com/database/technologies/datawarehouse-bigdata/dataminer.html

Categories and Features

Data Analysis

Not specified

Data Mining

Not specified

Machine Learning

Not specified

Predictive Analytics

Not specified

Categories and Features

Data Mining

Fraud Detection
Predictive Modeling
Statistical Analysis

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