Partition Benefits in SSAS

There are following benefits of using partition in SSAS

Ø  Minimize downtime:  Cube partitioning supports reducing downtime associated with cube processing. In almost all the cases, a portion of data warehouse is volatile and needs to be processed often. However other portions are relatively static. For example, in a sales cube, we need to change the current year's data nightly, but sales from previous years might change only occasionally - in case if account for merchandise returns and exchanges. If your warehouse tracks last 10 years sales then processing only the current partition may be 10 times quicker than processing the entire cube.

Ø  Better Query Performance: Cube partition is a powerful mechanism for improving query performance. Queries that summarize data over 10 years could take considerably longer than those that only search through the current year data. If we have proper partitions then SSAS only has to scan a small subset of data to return query results hence dramatic performance improvements compared to queries running against a cube with a single partition.

Ø  Aggregations benefits: The partition queried frequently could benefit from additional aggregations, which in turn could improve performance. Partition(s) that are used less can be processed less frequently with considerably fewer aggregations.

Ø  Customized storage and processing settings: Frequently accessed partitions might benefit from proactive caching and ROLAP storage. On the other hand, other forms of storage and processing might be better for less frequently queried partitions.

Ø  Distributed query and processing load: SSAS allows you to create remote partitions - a remote partition resides on a server different from its parent cube. This way the queries that affect the remote partition are processed on a server separate from its parent cube, allowing you to take advantage of additional processing power.
Ø  Parallel Partitions Processing: SSAS allows processing multiple partitions in parallel on a server that has multiple processors. This can further reduce the total cube processing time