analysis environment support
Customize and manage Hadoop ECO software composed of various and complex open source technologies according to customer's analysis requirements.
High-speed storage processing
and real-time processing support
A distributed processing framework for large-scale data processing that allows distributed processing across multiple nodes, enabling fast data processing.
Data distributed storage
Distribute data across multiple nodes, replicate some of the data across nodes for increased reliability. Enables storage and access of large volumes of data.
System operation support
Enables vendor-agnostic big data platform construction through pure open source Hadoop deployment, with a robust response system tailored to customer needs, resulting in a perfect system.
Advanced pre-processing and real-time processing system
Realizes data processing without bottlenecks, from integrated data collection to processing, storage, analysis, utilization, and visualization.
Web-based integrated big data analysis environment
Provides a web interface for visualizing and analyzing data, and enhances user convenience by providing various necessary tools and libraries via a web-based platform.
Provisioning monitoring platform management function
Offers automatic provisioning and configuration of Hadoop clusters, and monitors their performance, availability, and stability, providing alerts to aid in fault response.
Cost efficiency with open source
RNTire BDP is more cost-effective than existing commercial data processing solutions. Since it is based on open source, there are no license costs, and it can perform large-scale data processing without being constrained by hardware.
Availability guarantee through distributed storage
RNTire BigData distributes data storage to enable data to be read from other nodes in case of errors in a single node, ensuring high availability.
- Web-based user interface
Provides an optimized work environment for various software executions through a web-based platform.
- Monitoring of H/W and S/W resources
By using the H/W and S/W resource statistical function, it is possible to check the analysis result of bottleneck and idle resources.
- Integrated Data Management
We provide a unified data environment and integrate research data for stability, usability, and convenience.
- S/W license management
You can manage and assign individual/departmental software licenses and monitor the overall application licenses for the system.
- Job scheduler
Slurm, a job scheduler based on Linux, is pre-installed by default.
- Configuration and management of specific resource groups
Various types of HPC resources (servers, VMs, GPUs, S/W, H/W, licenses) can be grouped and assigned to specific users and departments.