Distributed Data Set consist of data set from a single data subject or data occurrence group which is distributed. A data set is a collection of data from the any data source. This collection is usually presented in a table format. The column in the table contains particular variables while each of the rows contains a value corresponding to any given member of the data set in question.
The data set lists values for each of the variable. For example, the values may be the height and weight of an object or values of random numbers. The term data set has its roots traced to the mainframe field where it had a well-defined meaning.
Distribute data set refers to all the collections of data that are shared within a distributed database system. For example, in a data warehousing projects, the physical architecture of a data warehouse may be composed of more than one computers acting as both server and client depend on certain conditions. The system employs several computers because a data warehouse is the main repository of all kinds of enterprise data such as historical and current transactions data. Because of the heavy load of data that needs to be processed, one central computer, even if is the today’s equivalent to the distant past’s mainframe computer, may not be able to handle such heavy load.
Several databases are being employed to act both as data sources and at the same time load sharers in the warehouse system. As each of these computers can be relatively independent and independent to some degree from each other, they may have disparate data outputs. With that set, the data outputs are still a collection of data or the data set which the distributed data set.
Dealing with data sets can involve very complex processes. Basically, it means dealing with disparate distributed data sets from a wide array of sources. Real life data warehouse implementation, as opposed to the conceptual schema or logical schema which defines the data models, involves having data sources which powered by different kinds of database managements systems.
For instance, some database servers may be powered by Oracle, another may be powered by Microsoft and still others powered by open source database management systems like MySQL. Despite the fact these different database management systems are designed under one basic database theory, their implementations may include proprietary functions or data formatting. This can result in disparate distributed data sets.
Another problem with regards to distribute data set may be data disparity being caused by an enterprise information system whose design does not define very well a single, complete, integrated inventory of all its data. As a result, disparate distributed data set with data redundancy can negatively affect the performance of the information system in general and the database warehouse and data sources in general.
Distributed data sets within a large database implementation such as a data warehouse can be effectively managed with a distribute database management system.
While different computers work in different nodes with a network system and frequently shares distribute data sets, the Distributed Database Management System just sits in one central application actively managing all sorts of disparate data from and distributed data sets and transforming them into a uniform format that can be efficiently used by the data warehouse.
It also manages the synchronization of all data and data sets among all the nodes in the distributed database network system. This synchronization is extremely important because data needs to be consistent for the final data consumers, such as the chief executive officer or any company decision maker, to get accurate reports.