| 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. The term is often associated with streaming data architectures and real-time operational decisions that can be made automatically through robotic process automation (RPA) and policy enforcement. Real-time analytics software has three basic components -- an aggregator that gathers data event streams (and perhaps batch files) from a variety of data sources, a broker that makes data available for consumption and an analytics engine that analyzes the data, correlates values and blends streams together. The system that receives and sends data streams and executes the application and real-time analytics logic is called the stream processor. Real-time analytics often takes place the edge of the network to ensure that data analysis is done as close to where the data originated as possible. In addition to edge computing, other 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 -- combines hardware and software products designed specifically for analytical processing.
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. | 
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