.png)
.png)
AI Storage Platform
AI Storage
Platform
In the AI era, data is a key competitive advantage. Daewon CTS proposes solutions that enable customers to effectively leverage software-defined storage solutions and high-performance storage hardware within AI infrastructure architectures, optimized for their AI workloads.
In the AI era,
Storage Challenges
Future storage platforms will evolve beyond simply storing bits, to actively understanding and indexing data using AI, enabling AI models to query vast data sets in natural language and retrieve relevant information. Until this era of intelligent storage arrives, enterprises must address the storage challenges associated with AI workloads. To maximize the efficiency of AI projects, solutions to data storage and management issues are essential.
Challenge
Storage challenges arising
from increasing AI demands
Data volume and scalability
-
As data processed in AI projects grows rapidly, storage scalability issues arise.
-
Data silos and challenges in storage management are exacerbated.
-
The nature of AI workflows leads to increased data replicas, further raising storage requirements.
Data mobility and locality
-
Moving large-scale datasets imposes significant costs and time burdens.
-
Data movement and synchronization across diverse environments, such as edge devices, data centers, and the cloud, can create challenges.
-
High data transfer costs and latency in cloud environments can lead organizations to revert to on-premises solutions.
Management complexity
-
Distributed storage environments make unified data management difficult.
-
Necessary management practices, such as metadata management, data catalogs, and dataset version control, remain at early adoption stages.
-
The need to manage multiple elements—including multi-tenant data access, data governance, access control, and sensitive data anonymization—adds to complexity.
Service
Optimization services
DIA NEXUS focuses on
Scale-out storage
Guidance on adopting scale-out distributed storage systems to handle petabyte- and exabyte-scale data, and leveraging object storage or distributed file systems.
Data mobility
Guidance on building an integrated data fabric across edge, on-premises, and cloud environments to maximize data mobility.
Reducing management complexity
Proposing approaches to leverage a data management platform that integrates metadata management, data catalogs, and data governance capabilities.