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distributed data services examples
Redundancy means duplicacy of data or repetitive data. In distributed database case the data is stored in different systems . So the answers is yes there can be redundancy of records / data.In distributed database , data is stored in different systems. Since the data is distributed there is redundancy of records.
A distributed data warehouse is a type of data warehouse architecture where data is distributed across multiple servers or nodes in a network. This allows for improved scalability, performance, and fault tolerance compared to a centralized data warehouse. Distributed data warehouses can handle large volumes of data more efficiently by spreading the workload across multiple nodes.
distributed data base means noting
The easiest way to tell if data is normally distributed is to plot the data.line plot apex
The database can be of two types either distributed or centralized . In distributed database the data is in different systems. In centralized database the data is in a single central system.We can categorize database as either distributed or centralized . In centralized database the data is in a single centralized system. While In distributed database the data is in different systems .
Distributed databases in a DBMS are databases that are stored on multiple computers across a network. They allow for data to be spread out and accessed simultaneously from different locations, which can improve performance and scalability. Distributed databases can enhance fault tolerance and reduce the risk of data loss.
The mean and standard deviation. If the data really are normally distributed, all other statistics are redundant.
Fiber Distributed Data Interface. . .
Muhammad Idrees has written: 'Design and management of distributed data processing' -- subject(s): Electronic data processing, Distributed processing
box- and - whisker plot
The query processor in a distributed DBMS is responsible for receiving and analyzing queries from users or applications, determining how to execute the query across multiple distributed nodes, and coordinating the execution of the query to retrieve the desired data efficiently. It optimizes query performance by considering factors such as data distribution, network latency, and data transfer costs across distributed nodes.