Distributed Database Essay

Each distributed DB perform some procedures to protect the data from any threats may occur through transactions. First is access control methodology which prevent unauthorized access to data. Second, inference control which prohibit users from inferring confidential data of other individuals using queries. Finally, flow control to prevent information from flowing to unauthorized persons in a way that violates organization policies.

2) Database administration is more important but less difficult in multiuser database systems than in single-user database systems.

Also by these systems the company reduced its business cost and maintains the information and the data very updated form.

Replication: An approach to enhance consistency in large Database clusters. Ayyapa Reddy Pasam, ayyappayouth@gmail.com Abstract Database systems implemented in parallel computers is called Database clustering.

Lately with the development of distributed computing, issues services that utilizes web and require enormous amount of data come to forefront. For Organizations like Facebook and Google the web has developed has a vast, distributed data repository for which handling by conventional DBMS is not sufficient. Rather than extending on the hardware capabilities, a more realistic approach has been accepted. Technically, it is an instance of scaling through dynamic adding servers from the reasons increasing either information volume in the repository or the number of users of this repository. In this scenario, the big data problem is frequently examined and in addition explained on a technological level of web databases.

information in real time, and the reduction of servers and software costs can significantly reduce

Virtual database technology makes the Internet and other data sources behave as an extension of an RDBMS system. According to some estimates, 85 % of the world 's data is outside of relational database systems. Important data is fling across web sites and database systems. These data sources organize the data sources in different ways, in the vocabulary they use, and in their data-access mechanisms. Writing applications that combine data from these sources is a complex task as heterogeneity is involved.

This means that you can store a lot more data and process it at a lower cost with a lower latency.(Richards, 2016)

Our database management tool will help store data in one central location allowing better control and security and access. This entire process can be tracked and monitored easily, giving you better security.

- Resources such as databases and files shared on the network can be accessed extremely quickly.

In transactional workloads fault tolerant means that DBMS can recover from a failure without losing any data. In the distributed databases fault tolerances means that successfully commit transactions and make progress even in the worker node failures. For read-only queries in analytical workloads, query doesn’t have to be restarted if a case of one node’s query fails.In cloud there is a high failure rate. It can happen in single node failure during long query processing.

The amount of the data transmission is very vital in distributed approach unlike in the centralized approach. The tables EMP and DEPT in the paper provides analysis of performances of joins and semi joins in both distributed and centralized approaches. The tables DEPT and EMP are placed in the same location while analyzing the centralized database system and they are placed on different sites for distributed approach. The following assumptions are further made: relations in both cases are not fragmented, the query is requested from a different site, EMP has 14 tuples in total each of 51 bytes, and DEPT on the other hand has 4 tuples each of 29 bytes.

Distributed processing takes advantage of telecommunications by using a network to allow for remote processing equipment that can then receive data via the network (Stair & Reynolds, 2016, Basic Processing Alternatives section). There are many advantages to this form of computing over the standard centralized server that is often utilized. Natasha Gilani (n.d.) notes four benefits to this style of processing including lower cost, better reliability, improved performance and reduced processing time, and flexibility. The system is lower cost due to the lower cost of microcomputers over the cost of a mainframe, more reliable due to the processing being spread over multiple processors decreasing the reliability on one machine, has increased speed and performance because of the ability to spread the work over multiple processors and has better flexibility due to the ability to break up the computers over multiple locations and add or subtract with less issues (Gilani, n.d.).

The continuous developments in information and communication technology have recently led to the appearance of distributed computing environments, which comprise several, and different sources of large volumes of data and several computing units. The most prominent example of a distributed environment is the Internet, where increasingly more databases and data streams appear that deal with several areas, such as meteorology, oceanography, economy and others. The application of the classical knowledge discovery process in distributed environments requires the collection of distributed data in a data warehouse for central processing. However, this is usually either ineffective or infeasible for the following reasons:

A 'Data Search' feature using a Google-type search syntax translated transparently for user to SQL.