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Data Access
The challenge of data access is far more complicated than simply providing access to files for all users. Throughout the many approaches to this critical business need, there are a number of complications that arise: bandwidth usage, file coherence, version consistency, and file latency which can result in lost productivity. One of the most commonly used solutions for data access revolves around WAN acceleration technology.
This technology is favored for its ability to centralize data, which improves file coherence and simplifies back-up procedures. Unfortunately, limitations relating to bandwidth and file latency—the speed at which data travels across a WAN—ultimately cause delays in data access that are unacceptable for productivity. Several technologies today seek to address these limitations and reduce delays. Technologies have been developed to accelerate transfer speeds, decrease the size of files shared, and reduce application chatter. However, despite these improvements, the number of remote locations and the volume of data being shared among today’s fast-growing companies are still exceeding the capability of these advanced WAN technologies.
Some methods that have been introduced to address these limitations actually result in more complicated business processes. For example, the copying of key files from a central source to local servers in order to work on them can speed data access for users, but it creates new management tasks such as oversight, synchronization and file back-up at multiple sites to avoid work being incorrectly overwritten. Today it is becoming evident that the answer will not be found in an evolution of WAN acceleration technology, but by looking to a fundamentally different approach.
On the simplest level, the effectiveness of any data access solution revolves around its ability to reduce delays. The more capable a technology is at reducing delays, the more capable it becomes at handling large volumes of data and increasing numbers of sites.
Data Backup
Back-up copies of data that exist in one location, or in multiple locations, are critical to a company’s ability to survive catastrophic loss of data due to technology failure, fire, fl ood, or any other natural disaster. The same challenges that affect data access—the dispersion of data among multiple locations and the increasing volume of data—are also the very same challenges that complicate data back-up processes in today’s global business environment.
A key factor to consider in a data back-up solution is the potential for lost work if a scheduled back-up has to be utilized, despite the fact that work has been done since that last back up. This gap is known as the “data loss” window. A data loss window occurs when back-ups are intermittent, such as weekly, daily, or hourly. Regardless of the interval, in the event that a catastrophe destroys data and requires the use of backed-up data, the time-delay related to the back-up intervals equates to some degree of lost work.
To combat this issue, an emerging technology known as Continuous Data Protection (CDP) has recently been introduced in the IT field. While CDP is intriguing in concept, it suffers the same problems with bandwidth and file latency that also cripple WAN acceleration technology. Backing-up changed files on a continual basis across WAN networks creates bandwidth consumption that cannot be on a large scale. The resulting delays in file transfer speeds once again cause a data loss window to exist.
An interesting correlation is revealed, however, when we consider that the key limitations that affect CDP capability are directly linked to the delays in data access. Simply stated, the only data loss window that exists within a truly continuous data protection (CDP) solution is caused by transfer delays alone. Therefore, as delays in data access are reduced, the ability to back-up large volumes of data from multiple locations increases.
A Consolidated Solution.
The potential to reduce delays with a data access solution renders a direct impact on the ability to back-up large volumes of data with a minimal data loss window. With such an obvious overlap, the most efficient way to address both challenges is to implement a dual-purpose solution whereby the same mechanism solves both critical business needs.
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