An MPP Database (short for massively parallel processing) is a storage structure designed to handle multiple operations simultaneously by several processing units.
In this type of data warehouse architecture, each processing unit works independently with its own operating system and dedicated memory. This allows MPP databases to handle massive amounts of data and provide much faster analytics based on large datasets.
There are several types of MPP database architecture, each with its own benefits and drawbacks. Grid computing, for instance, uses multiple computers in distributed networks, and will use resources opportunistically based on availability. This reduces costs for server space, but also limits bandwidth and capacity at peak times or when there are too many requests.
Another popular method, computer clustering, reduces this problem by linking the available power into nodes that can connect with each other to handle multiple tasks. MPP databases are becoming more popular since smaller compute nodes can be connected to function toward a single goal.
This also reduces costs, as MPP databases can be scaled horizontally (simply adding more compute nodes on a server) as opposed to vertically (adding more servers for processing).
What Can I Use MPP Databases For?
The amount of data organizations produce today means that companies cannot rely on single servers or must pay handsomely for physical server capacity to handle massive datasets. Instead, MPP is becoming an increasingly popular alternative in a variety of settings.
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In business intelligence, for instance, MPP databases mean that more people in an organization can run their own data analyses and queries simultaneously without experiencing lag or longer response times. Especially for larger organizations, this degree of flexibility grants more stakeholders information on demand.
MPP databases are also useful for centralizing data in a single location. Instead of having to break up massive datasets, MPP allows them to be stored in a single location and accessed from different points. This includes storing a variety of data such as marketing, web, operational, logistics, and HR data.
For larger organizations, this centralized resource makes it easier to uncover insights, connect data dots that may not be apparent at first, and even build dashboards that contain more relevant information than those built from data that is fragmented. Finally, MPP is usually best suited to handle structured data sets as opposed to models such as data lakes.