If your organization is planning to invest in AI initiatives this year, you’re likely also looking at options for storing the massive amounts of data required to train your AI models. But when evaluating AI storage solutions, which factors matter the most?
In this post, we’ll dive into the five key features every IT decision-maker should look for when choosing the right data storage solution to meet their AI storage needs for decades to come.
1. Exabyte-Scale
AI datasets are really big and growing at exponential rates. An AI storage solution should allow organizations to independently scale performance and capacity in a single massive namespace, such as by utilizing ultra-dense, low-cost flash to handle any workload. In general, multiple system characteristics should be combined to support this type of limitless scale, including:
Disaggregated Architecture
By provisioning and maintaining storage needs and compute power separately, a leading AI storage solution ensures optimal resource speed, availability, and scale capacity for organizations and their AI initiatives.
Global Namespace
An AI storage platform should offer one global namespace that contains all your data, on-premise or in the cloud, eliminating data silos and streamlining data management. Once data is written, it should be immediately available in any location, with flexible synchronization policies to address various use cases.
Affordability
An AI data storage solution can only be scaled if it’s affordable. Therefore, the combination of a system’s lean, single-tier design, intelligent data reduction technology, and flexible multi-tenancy should bring the cost of flash in-line with Hard Disk Drive (HDD) storage — helping organizations to linearly scale performance and capacity as needed from petabytes to exabytes.
2. Maximum Performance
Above all, any infrastructure used for AI data storage needs to be able to comfortably handle the volumes of data that AI requires without slowing down the AI engine or impacting other internal systems. This high-performance requirement applies to both the ingestion and analysis of huge amounts of structured or unstructured data.
To achieve top-tier speed and performance, an AI storage solution should feature:
Single-Tier Design
Eliminating additional data storage tiers creates a simplified storage infrastructure that unifies data environments into a single, resilient tier of flash, providing complete freedom of data access and usage.
Flash-Based Storage
Flash storage is the preferred technology for AI data storage in order to quickly store, read, and write large amounts of data without any moving parts, offering better speed and performance than traditional spinning-platter hard drives.
Multi-Protocol
To meet the performance requirements of Al workloads, all data from edge-to-cloud should be able to be read or written using industry-standard File (NFS, SMB), Object (S3), and Kubernetes Container Storage Interface (CSI).
3. Enterprise Capabilities
For large organizations pursuing AI initiatives, an additional set of important AI data storage requirements emerge. To safeguard critical company data and ensure consistent 99.999+% uptime, enterprise-level organizations should consider advanced AI storage features such as multi-tenancy, Quality of Service (QoS) controls, flexible snaps, data catalogs, enhanced system security measures, and optimized backup support to prioritize and guarantee performance for mission-critical workloadst. To expand on each of these capabilities further:
Security
The right AI storage platform provides enterprise-grade security for all structured and unstructured data storage needs, making it simple for large organizations to modernize their data center infrastructure and fortify their data protection strategy.
Multi-Tenancy
An AI data storage platform should offer support for multi-tenant architecture, allowing enterprises to simplify deployment and achieve further system cost reductions.
Flexible Data Snapshots
The ability to quickly and easily snapshot data, at any scale, is an important feature for enterprise companies in order to consistently preserve data records. Ideally an AI storage solution should be able to perform this task without impacting system performance.
Backup Support
For enhanced protection from ransomware and other cyber attacks, an AI storage solution should support industry-leading backup applications, provide fast backups, and enable immediate recovery from large-scale attacks.
4. Data Reduction
To maximize system efficiency, an AI storage solution should come with global data reduction capabilities and efficiency algorithms that provide storage-level power and cost savings. Two complementary techniques that use computing power to reduce the size of stored data are compression and deduplication:
Compression: Reduces repeated copies of small bit patterns over a limited range.
Deduplication: Eliminates repeated patterns in much larger blocks over correspondingly larger sets of data.
At a minimum, an AI storage solution should employ both compression and deduplication to continually optimize storage use and decrease costs. However, some AI data storage platforms feature additional data reduction techniques that can further improve upon the storage benefits seen with the above approaches.
For example, VAST Data uses both compression and deduplication, and also adds a new technique called similarity reduction — a unique combination of data reduction and fine-grained pattern matching that reduces the amount of space needed to store data chunks that are similar, but not identical, to existing chunks. Similarity reduction goes beyond traditional data reduction methods by uncovering correlations in data that others miss, thus delivering the world’s first exabyte-scale reference compression system.
5. Simple Administration
Beyond being fast, secure, affordable, and scalable, an AI data storage platform also needs to be easy to use. Best-in-class platforms should feature a single, unified graphical user interface (GUI) for intuitive workload monitoring and analytics, plus secure administrative access with multi-factor authentication. Consider the following administrative capabilities when evaluating AI storage solutions:
User-Friendly Interface
A good, full-featured GUI allows system administrators to quickly understand the health of their storage system in seconds, and makes it easy to perform simple maintenance tasks or drill down into dashboard data.
Accessibility
The AI storage platform’s GUI should be easy to access from any modern web browser, without requiring specific plug-ins or languages such as Java or Adobe Flash. It also should have appropriate security layers in place so that it is only accessible to users with the required permissions.
Data Analytics
An AI storage solution should offer detailed data analytics, allowing users to understand what’s happening both at the system and application level. It should also come with customization features, enabling the creation of custom dashboards displaying key KPIs for the business.
Remote Upgrades
Being able to receive automated, online platform updates and expansions is an important feature of any AI data storage system in order to avoid the time and hassle of manual system upgrades.
VAST Data: Built for AI Storage at Any Scale
VAST Data is the data platform software company for the AI era. Designed from the ground up to make AI simple to deploy and manage, the VAST Data Platform streamlines data pipelines, accelerates time-to-insight, and delivers scalable AI storage performance for companies across the world.
Here’s how the VAST Data Platform addresses delivers on the five essential AI data storage features:
Maximum Performance: VAST Data’s Disaggregated, Shared-Everything (DASE) architecture eliminates the usual tradeoffs between performance, scale, and cost, providing industry-leading data speed and availability from edge to cloud.
Enterprise Capabilities: The VAST Data Platform was built to handle and support every possible enterprise requirement and use case, and is already relied upon by some of the world’s largest corporations.
Data Reduction: VAST Data’s similarity reduction technology can yield a 3:1 data size reduction improvement for AI training pipelines compared to compression and deduplication alone, plus 2X smaller backups than legacy data protection appliances.
Exabyte-Scale: The DASE parallel distributed system has been designed to support the scale and ambition of the world’s largest clouds, AI innovators, and data analytics environments by redefining the economics of flash storage.
Simple Administration: The easily-accessible GUI provides administrators with complete centralized control, as well as the VAST Analytics dashboard featuring a rich set of metrics to monitor everything from latency, IOPS, throughput, capacity and more — all the way down to the component level.
There’s a reason why VAST is now the fastest-growing data infrastructure company in history. Schedule a personalized demo today and experience first-hand how AI storage with the VAST Data Platform can help you bring your AI projects to life.
