Distributed computing is not required for all computing solutions. Consider that the business doesn't have any time constraints in system processing and an asynchronous remote process can do the job efficiently in the expected time of processing. Then, why should the business invest in having either a more competent resource or a distributed environment to bring performance improvements in such a process?

Over the past few years, organizations are looking at reducing costs and are investing in critical and essential business needs. This means that, if there is a real need for processing a complex data analysis and the organization has a number of additional resources available, it can make use of distributed computing wherein each computing resource, with its own memory, can process a bit of the complex analysis and contribute to its completion.

Not only that; instead of having a single huge computing resource, if we utilize multiple commodity hardware to act as a distributed system, this also adds load balancing resource optimization with an enormous cluster of nodes.

System software, such as Zookeeper, is discussed in detail in the next section and is available to effectively handle the resource load and fault tolerance with built-in rules. This has changed the economics of big data processing by bringing distributed systems into the picture.

Moreover, latency in data analysis and processing with big data impact the results a lot, especially due to ever changing data trends such as social networking. In such scenarios, big data computing processes should have minimum latency to follow market trends in its processing. Distributed systems in such a scenario makes a big difference by introducing multiple scalable resources for computation.

Organizations such as Amazon, Yahoo, Google, and Facebook are on this track, and this is how they can bring you the expected and matching content in fractions of seconds from such a huge database.