Pros and Cons of 3 Common Data Reduction Strategies
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Data reduction strategies can help enterprises create extra capacity in your current environment to manage ever-expanding company data. While data reduction may not reduce current data storage costs, it can help decrease the amount of required storage capacity in a SAN environment within any size enterprise.
1. Thin Provisioning
Pro: Thin provisioning helps your enterprise make more efficient use of existing storage capacity by eliminating the reserve on unwritten blocks of storage. This is one of the main benefits of thin provisioning over thick provisioning.
By deploying a thin provisioning strategy, enterprises can achieve storage savings of up to 30% with little to low impact on operations. Thin provisioning is available from a variety of vendors so enterprises of all sizes can take advantage of the benefits.
Con: The downside of thin provisioning is that it does not actually impact the written data. While current capacity will be used more efficiently, the existing written data is not altered or optimized to provide a greater amount of space in your storage capacity.
2. Data Deduplication
Pro: Data deduplication identifies repeated data patterns and reduces them to a single instance to help save capacity in the storage environment.
Because it reduces repeat patterns to a single physical copy, storage savings from deduplication can range from 2:1 to 10:1 depending on the data and the industry.
Con: Different deduplication processes may require more capacity in order to function properly. For instance, post processing deduplication requires more space to hold new data before it is deduplicated and can limit the amount of actualized saved space in your storage capacity.
Pro: Compression is a tried-and-true data reduction technique that offers significant space savings and has been in existence for almost 25 years. Originally used in IBM tape drives, compression optimizes data by finding repeat patterns of similar information that can be replaced with a more streamlined data structure. Compression can offer variable rates of space savings because data reduction is dependent on the specific type of data.
Con: Compression can introduce latency into both the read and write times for your data. Latency can be introduced to read times because compressed data must be rehydrated before it can be accessed, while write times can also be affected by compression algorithms.
Interested in a data reduction strategy? Learn how data storage management services from Park Place Technologies can help improve your storage efficiency and reduce your storage management headaches.