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In-memory analytics is a business intelligence (BI) methodology used to solve complex and time-sensitive business scenarios. It works by increasing the speed, performance and reliability when querying data.
Business Intelligence deployments are typically disk-based, meaning the application queries data stored on physical disks. In contrast, with in-memory analytics, the queries and data reside in the server’s random access memory (RAM). In-memory analytics is achieved through the growth and adoption of 64-bit architectures, which can handle more memory and larger files compared to 32-bit–and an overall reduction in the price of memory.
In-memory analytics helps improve the overall speed of a BI system and provides business-intelligence users with faster answers compared to traditional disk-based business intelligence, especially for queries that take a long time to process in a large database.
There are a number of in-memory analytics tools and technologies with different architectures. Boris Evelson (Forrester Research) defines the following five types of business intelligence in-memory analytics:
- In-memory OLAP: Classic MOLAP cube loaded entirely in memory
- In-memory ROLAP: ROLAP metadata loaded entirely in memory.
- In-memory inverted index: Index, with data, loaded into memory.
- In-memory associative index: An array/index with every entity/attribute correlated to every other entity/attribute.
- In-memory spreadsheet: Spreadsheet like array loaded entirely into memory [source: Forrester Blog]