Tiered storage is generally accepted as the prime methodology to reduce data storage costs and improve storage efficiency. Its objective is to minimize storage costs by storing data on a range of different storage media that balance performance, functionality and capacity needs.
Traditionally, data storage has been defined by the technology leveraged to protect data using a hierarchical pyramid structure, with the top of the pyramid designated for SSD to store ‘hot’ data, SATA HDDs used to store ‘warm’ data and tape used for the base of the pyramid to archive ‘cold’ data.
A New Storage Paradigm
As data usage become more complex, with increasing scale, levels of collaboration and diversity of workflows, users are being driven toward a new model for data storage. The new paradigm combines a file-based Primary Tier and a second, object-based Perpetual Tier. The Primary Tier (or Project Tier) holds all in-progress data and active projects. It is made up of flash, DRAM, and high-performance disk drives to meet the requirements of critical data workflows dependent on response time. The Perpetual Tier combines multiple storage media types – including any combination of cloud storage, object storage disk, network-attached storage (NAS) and tape – to address data protection, multi-site replication (sharing), cloud and data management workflows. Data moves seamlessly between tiers as it is manipulated, analyzed, shared and protected.
The distinction is important because the new two-tier paradigm focuses on the actual usage of data, rather than the technology on which it resides.
The Process of Tiering: Data Management
To effectively employ a tiered data storage strategy, it is necessary to determine which storage tier best suits a given class of data. Not only that, but often as data ages, it must be reclassified on a regular basis, as data storage requirements may evolve over time.
The activity of ensuring data is on the correct storage tier at the right time is called data management. Because a manual approach to data management is virtually unsustainable, most businesses rely on automated software for this process. In addition to monitoring data throughout its lifecycle and moving it to lower cost tiers based on age, modern data management software provides the policy engines for IT to create the right data movement process for its many workflows and varied operational needs.
The Cost Problem
The high cost of data management software and the way that vendors calculate return on investment (ROI) can often punish customers – pricewise – for starting small. False ROI assumptions from data management vendors discount the user’s ability to reuse existing storage equipment, overlooking the fact that the hardware from which they are moving data is already purchased. Their calculations cost compares a user’s present data storage predicament with their ideal storage infrastructure without fully accounting for the role of their current architecture in a realistic transition plan that accommodates data growth gradually. In many cases, the cost of the data management software can rise up to be one-and-a-half to two times the cost of secondary storage hardware in a new solution.
Migration of data from primary storage to lower cost data repositories is a key component of any data management strategy, but its cost benefits are often negated by the high price of data management software.
Learn how to address this cost problem in a white paper written by storage analyst firm, Storage Switzerland, entitled “Reducing the High Cost of Data Management”.