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What level of data integrity is achieved? The possibility of two files producing the same hash value is extremely remote, but if that's unacceptable, you need to look past hash-based solutions. Also ask vendors to explain if data grooming occurs in the background to ensure data integrity and recoverability.
What about performance? If backup speed is a critical issue, you should examine the throughput speeds of inline products to ensure they're adequate. You may be better off with a product that performs data reduction after backups are complete.
How scalable is the product, and what happens if a single appliance maxes out? The scalability of data-reduction products varies considerably. Avamar uses the redundant array of independent nodes (RAIN) architecture to scale; Diligent uses clustering; ExaGrid, HP RISS and Sepaton use grid principles to grow their appliances to larger capacities; and Data Domain uses a single-appliance concept. Management may be a concern as well, as the system grows to multiple appliances.
How big is the index? If data commonality checks are done in memory, the size of the index matters. With a small index like Diligent's, all searches can be done on a single server, which improves performance. If a product requires the index to be large or distributed, how it coordinates the parts may be an issue.
Is the degree of data reduction acceptable? In general, you should expect data reductions of
An evolving technology
Data protection is undergoing a sea change with the number and type of products hitting the market at an unprecedented level. Given the extreme data growth rates, adding disk-based data protection is no longer an option. But picking the right technology has never been more difficult. The fundamentals of data reduction should provide enough knowledge to ask the right questions and seek straight answers from vendors.
This was first published in July 2006