Evaluating storage products for your enterprise needs

For IT departments, evaluating storage products is an important process that can determine the entire shape of a company’s digital infrastructure. A poorly architected storage solution can substantially impair a department’s performance and lead to major outages or, in worst case scenarios, permanent data loss. However, an intelligent decision, made with the right factors in mind, can provide an organisation with a scalable shared storage solution that is able to meet the performance and reliability service-level objectives of the proposed system design.

Especially on a larger scale, maintaining IT architecture can be like keeping an old car operational. It’s expensive and resource intensive if you don’t have the time or money to source a better alternative. IT sysadmins working with antiquated and inefficient hardware can struggle to catch up and support data transformation initiatives.

Enterprise data requirements

Understanding the block, file and object requirement

When onboarding new applications, it’s important to understand the kind of data being stored to make an informed decision about whether to use block, file or object storage.

Block storage is the most common use case for DAS and SAN environments. In the case of DAS, an entire RAID volume or physical drive is presented to the OS as a raw, unformatted volume. In the case of SAN environments, the entire LUN (compromised of several physical drives) presented from the storage array is presented to the OS through a high-speed network and appears as a raw unformatted volume. The underlying layers of the raw volume consist of smaller extents or sectors that the operating system handles and then the underlying storage subsystem is able to map those logical blocks to specific physical blocks on the specific drive(s). Block-level storage is fast, reliable and most ideal for continuously changing data like relational databases, online transaction processing (OLTP) databases, email servers or virtual desktop infrastructure, where high transaction throughput and low latency are requirements.

Object storage stores data (and the associated metadata) in containers with unique identifiers, with no folders or subdirectories like those associated with file storage. It uses the concept of key-value stores, where each key points to a specific “value” or piece of data and is retrieved via APIs.

It is mainly used to handle large amounts of unstructured data, like emails, backup images, video surveillance footage or, in IoT, data management for machine learning and data analytics. Object storage is good for handling very large amounts of data and can scale as quickly as the application requires but is slow at data retrieval, making it inefficient for databases or high-performance computing. Examples of object storage are Amazon S3, Google Cloud object storage or Azure Blob storage.

File storage stores data in files, organised in folders and subdirectories, and is shared over a network using SMB (Windows) or NFS (Linux). It’s good for centralising storage files like videos, images or documents but has limited scalability as the amount of data continues to grow. It is not the most suitable application to handle very large amounts of unstructured data or continuously changing data like OLTP databases.