The process of data distribution is not just a process of replicating data at more than one data site in order to develop and maintain an integrated data resource that ensures data are properly managed.
It is also one of the key aspects whereby a parallelizing compiler for a distributed memory architecture has to give serious consideration so that the system can achieve optimal efficiency.
With a dynamic data distribution system, the access time to local or remote data can be one be one or various orders of magnitude different which means a significant and dramatic effect in performance.
Finding the best way for data distribution often depends on the program structure and data sets as well as capabilities of the compiler and the characteristics of the machines where it will be implemented.
There are many different automatic data distribution tools to assist a database or data warehouse administrator in finding the best dynamic data distribution and parallelization strategies to give the best performance of an information system.
In order for these tools to optimize performance without sacrificing data quality and integrity, the seriously take into account crucial aspects of the system such as the data movement, parallelism and load balancing.
In large data warehouses with several data sources and different computationally intensive phases may occur, a dynamic data distribution may be complemented with remapping of actions between phases so as to increase the system efficiency.
Dynamic data distribution technologies have evolved with the every increasing data demands by business organizations in today’s information-driven business environment.
High level architecture data distribution management services are becoming more common and they have been geared and designed for reducing the amount of data traffic received by individual simulations in large scale distribution systems.
One of the most common techniques for optimizing dynamic data distributions performance is using multicast groups for sending data to certain subsets of potential receivers.
There are dynamic data distribution management tools specifically designed for distributed and dynamic architectures. These tools are flexible and can provide general purpose specification of filtering disparate data and managing the data traffic in the network effectively. The optimization benefit from these dynamic data distribution management tools can improve overhead of the interest management itself.
Implementing a dynamic data distribution feature means investing more through additional computer hardware with more powerful features and network peripherals capable of heavy volume traffic.
Since in this dynamic environment, distributed data need to be continually evaluated and adjusted to meet the business information demand in an optimum manner, this could mean more data replication to be handled separate by different computers within the different nodes of the system. This would also mean that a better software application will be employed to handle the new and bigger challenge.
A dynamic data distribution feature should be able to scale with a dynamically changing business rules which is a typical nature of a progressive business organization.
For example, a fast rising company will potentially make new acquisitions within the next few years or will add a couple of new products and marketing scheme within the next couple of months so new company set up may ensure which means new data rules may be formed. This should be considered by the whole data architecture and of course by the physical information system implementation.
Information technology will continue to move forward in a dynamic manner. It will never revert. So, it is always a wise decision to invest in an information system that is generally dynamic.