Glossary

AI Data Architecture

AI data architecture is the framework used to organize, store, manage and deliver the data required by artificial intelligence (AI) and machine learning (ML) workloads.

It defines how data moves from source systems into storage and processing environments where it can be prepared, accessed and used for model training, inference, retrieval and other AI operations.

Unlike traditional data architectures designed primarily around databases, business applications and analytics, AI data architecture must accommodate large volumes of unstructured data, rapidly growing datasets and highly parallel access patterns. It may need to support billions of files or objects while providing consistent access to data across AI pipelines and infrastructure.

An effective AI data architecture also addresses data protection, security, governance and lifecycle management. These capabilities help organizations maintain usable and trustworthy datasets as AI environments grow.

Why is AI data architecture important?

Data infrastructure can have a direct effect on the performance, scalability and operational efficiency of AI systems. GPUs and other accelerators require a steady supply of data, and storage bottlenecks can leave expensive compute resources waiting for input.

AI projects also tend to create multiple copies and versions of datasets. Raw source data may be cleaned, transformed, labeled, embedded or combined with other information before it reaches a model. Training can generate checkpoints and model artifacts, while inference and retrieval workflows introduce additional data access requirements.

A well-designed AI data architecture provides a consistent foundation for these workflows. It can help organizations:

• Keep data accessible across AI development and production environments
• Scale storage independently from compute resources
• Reduce unnecessary data movement and duplication
• Support high-throughput, parallel access to large datasets
• Protect AI data against ransomware, accidental deletion and other threats
• Apply governance and lifecycle policies across growing data repositories
• Preserve data for future models and AI use cases

What are the components of AI data architecture?

AI data architectures vary by organization and workload, but several components are commonly involved.

Data sources

AI data can originate from databases, applications, sensors, devices, logs, documents, images, video, audio and other enterprise or external sources. Increasingly, much of the information used by AI is unstructured.

The architecture needs mechanisms for ingesting this information while preserving the metadata and context required for downstream processing and governance.

Data storage

Storage provides the persistent foundation for AI datasets, model artifacts, checkpoints and other information generated throughout the AI lifecycle.

Object storage is frequently used for large-scale AI environments because it can accommodate very large quantities of unstructured data and scale capacity without relying on traditional file system hierarchies. Depending on the workload, file, block and cloud storage may also form part of the overall architecture.

Data processing

Before data can be used by an AI model, it often needs to be cleaned, normalized, transformed, labeled or enriched. Processing frameworks perform these operations and create datasets suitable for training, fine-tuning, retrieval or inference.

The storage layer needs to support efficient access throughout these processing stages to avoid unnecessary copying and movement of large datasets.

Compute infrastructure

AI compute infrastructure includes CPUs, GPUs and other accelerators used to process data and execute AI workloads.

Storage and compute are closely related but can scale independently. Separating the two allows organizations to increase GPU resources without creating unnecessary storage silos, or expand data capacity without adding compute resources.

Metadata and data management

Metadata helps organizations understand what data they have, where it originated, how it has been processed and how it can be used.

Metadata management, cataloging and indexing can make large AI repositories easier to search and govern. These capabilities become particularly important as organizations accumulate datasets across multiple AI projects.

Data security and protection

AI datasets can contain proprietary, regulated or otherwise sensitive information. They may also represent substantial investments in collection, preparation and labeling.

Security controls such as encryption, access management and auditing help protect this information. Data protection capabilities such as immutability, versioning and isolated recovery copies can improve resilience against ransomware, malicious modification and accidental loss.

How does data flow through an AI data architecture?

An AI data pipeline typically begins with data ingestion. Information from multiple sources is collected and written to a storage environment where it can be retained in its original form.

Processing systems then access the data to clean, transform, classify or enrich it. The resulting datasets can be used for model training or fine-tuning. During training, models repeatedly access data while producing checkpoints and other artifacts that also need to be stored.

After deployment, AI applications may continue to access enterprise data during inference. Retrieval-augmented generation (RAG), for example, allows applications to retrieve relevant information from organizational datasets and provide that context to a large language model (LLM).

The same underlying data may therefore participate in multiple workflows over its lifetime. Designing an architecture around shared, durable storage can reduce the need to create separate data silos for each stage.

What storage requirements does AI data architecture create?

AI workloads place several demands on storage infrastructure.

Scalability: Training datasets and AI-generated data can grow rapidly. Storage needs to scale in both capacity and object count without introducing operational complexity.

Performance: GPUs require fast access to data. Storage must provide sufficient aggregate throughput to support many parallel compute processes.

Concurrency: Multiple applications, users and compute nodes may need simultaneous access to the same datasets.

Durability: AI datasets and model artifacts can be expensive or difficult to recreate, making long-term data durability important.

Security: Storage must protect sensitive and proprietary information while supporting appropriate access controls.

Resilience: Immutable data and recovery mechanisms can help organizations recover from ransomware, accidental deletion or other destructive events.

Lifecycle management: Organizations need ways to manage large quantities of data over time without manually moving information between infrastructure silos.

What is the role of object storage in AI data architecture?

Object storage can serve as a scalable data foundation for AI workloads, particularly when organizations need to manage large quantities of unstructured information.

Rather than organizing information through a conventional file hierarchy, object storage manages data as objects containing the data itself, metadata and a unique identifier. This approach supports very large namespaces and makes it possible to scale repositories to billions of objects.

Modern object storage can also provide the throughput and parallelism required by data-intensive AI workloads. Using a shared object storage layer can allow multiple AI applications and compute clusters to access common datasets while reducing the need to maintain separate copies for each environment.

Support for the S3 API can also simplify integration because many AI, analytics and data-processing applications already support S3-compatible storage.

How does AI data architecture support RAG?

Retrieval-augmented generation connects generative AI applications with information that exists outside the model’s original training data.

In a RAG architecture, enterprise information is collected and prepared for retrieval. Relevant content may be divided into smaller chunks, converted into vector embeddings and indexed so applications can identify information related to a user’s query.

The original documents and other source data still need reliable storage. An AI data architecture can provide persistent storage for source datasets while supporting the processing and retrieval systems used by the RAG pipeline.

This approach allows organizations to retain control of their source information and update knowledge repositories without retraining an entire foundation model.

How does AI data architecture support data security?

AI infrastructure expands the number of systems that interact with organizational data, which can increase the potential attack surface. Training datasets, model artifacts and source repositories therefore need security controls throughout their lifecycle.

An AI data architecture can incorporate:

• Role-based access controls
• Encryption for data at rest and in transit
• Authentication and authorization
• Audit logging
• Data immutability
• Versioning
• Geographic or administrative isolation
• Backup and recovery policies

Immutability is particularly relevant to cyber resilience because it prevents stored objects from being modified or deleted during a defined retention period. Combined with appropriate isolation and recovery procedures, immutable storage can help preserve clean data copies following a ransomware attack.

What is the difference between AI data architecture and traditional data architecture?

Traditional enterprise data architectures often center on transactional databases, data warehouses and business intelligence systems. These environments primarily manage structured data and predictable application workloads.

AI environments introduce a different set of requirements. They frequently rely on very large collections of unstructured data, highly parallel processing, GPU infrastructure and iterative workflows that repeatedly read and transform the same datasets.

AI data architecture therefore places greater emphasis on scalable unstructured data storage, high-throughput access, metadata, data portability and integration with distributed compute frameworks.

Traditional data platforms can remain part of the architecture, particularly as sources of structured business information. The goal is to provide an infrastructure layer capable of supporting both existing enterprise data and newer AI-specific workflows.

What should organizations consider when designing an AI data architecture?

Organizations should begin with the data rather than focusing exclusively on AI compute infrastructure. Important considerations include the volume and type of data available today, expected growth, where data is generated and which applications need access to it.

The architecture should also account for:

• Expected training and inference workloads
• GPU and accelerator requirements
• Structured and unstructured data sources
• Storage capacity and performance requirements
• On-premises, cloud and hybrid infrastructure
• Data sovereignty and compliance requirements
• Security and cyber resilience
• Metadata and governance
• Application and protocol compatibility
• Long-term data retention and lifecycle policies

Planning these elements together can reduce infrastructure fragmentation as AI projects move from experimentation into production.

How does Scality support AI data architecture?

Scality provides enterprise object storage designed for large-scale unstructured data environments. Scality RING can serve as a persistent data layer for AI and ML workloads, providing scalable capacity and S3-compatible access for applications and processing frameworks.

By separating storage from AI compute infrastructure, organizations can maintain shared data repositories that support multiple GPU clusters, applications and workflows. This can reduce unnecessary copies of large datasets while allowing storage and compute resources to scale independently.

Scality also provides security and cyber-resilience capabilities designed to protect enterprise data, including immutability and access controls. These capabilities can help organizations build AI data architectures that address performance, scalability and data protection requirements across the AI lifecycle.

Frequently asked questions

What is an AI data architecture?

An AI data architecture defines how data is collected, stored, processed, protected and delivered to AI and machine learning systems.

Why is storage important for AI?

AI systems can process very large datasets in parallel. Storage needs sufficient capacity, throughput and concurrency to keep compute resources supplied with data while preserving datasets and model artifacts.

What type of storage is used for AI?

AI architectures can use object, file, block and cloud storage depending on workload requirements. Object storage is commonly used for large repositories of unstructured data because of its scalability and compatibility with S3-based applications.

How does object storage support AI workloads?

Object storage provides scalable capacity for large datasets and can support parallel access from distributed AI applications and compute infrastructure. S3 compatibility also enables integration with many AI and data-processing tools.

What is the difference between an AI data platform and AI data architecture?

AI data architecture describes the overall design for how data moves through and supports an AI environment. An AI data platform is a set of technologies and services that implements parts of that architecture, such as storage, processing, governance and data access.

How does AI data architecture improve cyber resilience?

AI data architecture can incorporate encryption, access controls, auditing, immutable storage and recovery mechanisms to protect datasets and model artifacts from ransomware, malicious changes and accidental loss.