Massively Parallel Processing Defined
Massively parallel processing (MPP) is a storage structure designed to handle the coordinated processing of program operations by multiple processors. This coordinated processing can work on different parts of a program, with each processor using its own operating system and memory. This allows MPP databases to handle massive amounts of data and provide much faster analytics based on large datasets.
MPP processors can have up to 200 or more processors working on application and most commonly communicate using a messaging interface. MPP works by allowing messages to be sent between processes through an “interconnect” arrangement of data paths.
There are several types of MPP database architectures, each with their own benefits:
- Grid computing– uses multiple computers in distributed networks. This type of architecture uses use resources opportunistically based on their availability. This architecture reduces costs for server space, but also limits bandwidth and capacity at peak times or when there are too many requests.
- Computer clustering – links the available power into nodes that can connect with each other to handle multiple tasks at once.
Advantages of MPP databases include:
- Allowing more people in an organization to run their own data analyses and queries simultaneously without experiencing lag or longer response times.
- Centralizing data in a single location.
- Making it easier to uncover insights, and build dashboards that contain more relevant information than those built from data that is fragmented.
In Data Defined, we help make the complex world of data more accessible by explaining some of the most complex aspects of the field.
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