Data Distribution Techniques - DDBMS

Data Distribution Techniques - DDBMS

The major goal of a Distributed Database System (DDBS) is to maintain better control of the organization's data. The data is distributed at different sites based on the access patterns and costs. The best option can be selected by comparing the costs for different data allocation options. The various techniques used in distributed databases are as follows:
  • Data Distributed Techniques
    • Data Fragmentation
    • Data Replication 
    • Data Allocation

Data Fragmentation:

Data fragmentation allows one to break a single object into two or more segments or fragments. The object might be a user's database, a system database, or a table. Each fragment can be stored at any site over a computer network. Information about data fragmentation is stored in the Distributed Data Catalog (DDC), from which it is accessed by the transaction processor to process user requests.

Data Replication:

A replicated database is a distributed database in which multiple copies of some data items are stored at multiple sites. 
The main reason for using replicated data in distributed systems is to increase Database System (DBS) availability. By storing critical data at multiple sites, a copy of the data item is still available on another site if one of the sites fails.