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Real-time analytics is the use of, or the capacity to use, data and related resources as soon as the data enters the system. The adjective real-time refers to a level of computer responsiveness that a user senses as immediate or nearly immediate. Real-time analytics is also known as dynamic analysis, real-time analysis, real-time data integration and real-time intelligence.
Technologies that support real-time analytics include:
- Processing in memory (PIM) -- a chip architecture in which the processor is integrated into a memory chip to reduce latency.
- In-database analytics -- a technology that allows data processing to be conducted within the database by building analytic logic into the database itself.
- Data warehouse appliances -- combination hardware and software products designed specifically for analytical processing. An appliance allows the purchaser to deploy a high-performance data warehouse right out of the box.
- In-memory analytics -- an approach to querying data when it resides in random access memory (RAM), as opposed to querying data that is stored on physical disks.
- Massively parallel programming (MPP) -- the coordinated processing of a program by multiple processors that work on different parts of the program, with each processor using its own operating system and memory.
Applications of real-time analytics
In CRM (customer relations management), real-time analytics can provide up-to-the-minute information about an enterprise's customers and present it so that better and quicker business decisions can be made -- perhaps even within the time span of a customer interaction. Real-time analytics can support instant refreshes to corporate dashboards to reflect business changes throughout the day. In a data warehouse context, real-time analytics supports unpredictable, ad hoc queries against large data sets. Another application is in scientific analysis such as the tracking of a hurricane's path, intensity, and wind field, with the intent of predicting these parameters hours or days in advance.