How does the Hadoop ecosystem work?
Apache Hadoop is often referred to as an ecosystem – from the seeds of big data, users can grow insights concerning some aspect of strategizing or decision-making and produce actionable techniques. Not content to rest on the laurels of initial insights, the cycle begins again, incorporating newly-developed conclusions and further sources of data. With applications of Hadoop clusters, each cycle becomes smarter and more direct, informed by the ones preceding it.
One of the reasons that Hadoop gets compared to an ecosystem, according to SiliconANGLE, is that it uses as much as it can of available resources and wastes as little as possible. Having data accessible in a single architecture is one of the Hadoop's foundational principles. So how does the process unfold? By examining Apache Hadoop at each of the stages of a natural ecosystem, it's easier to see Hadoop at work.
The big data system starts with information production. Big data comes from various sources and can say many different things – sometimes showing contradictory information on the surface. The production process itself is indiscriminate – it makes everything available. Big data is truly massive, and includes irrelevant data, noise and other confounding factors that are of little use but to impede progress. Like any ecosystem, only some of what is produced will actually be useful to nourishing future strategic growth.
Hadoop installation can put the systems that structure this data into place. The open source framework makes Hadoop truly consumer-driven. Whatever data is appropriate for the environment will be used. Hadoop clusters make data relevant and easy to maneuver into actionable insights, even for those consumer-users who don't list 'data scientist' on their resumes.
It may sound counterintuitive to discuss the decomposition and erosion of data insights as a good thing, but the reality is that strategies need to be reformed all the time. Some decay is necessary to achieve the next level of growth. Hadoop has this sort of architecture built in, according to Fast Company contributor Scott Gnau, as its scalable infrastructure acts as a smart sieve, containing data that's needed and discarding information that has outlived its usefulness
To return to big data production, organizations must use strategies that effectively recycle data without also putting unnecessary data debris back into the system. In this effort, according to InfoWorld, data visualization, while not a rock star tactic, provides the ability to separate data out from the foliage. It also makes the next cycle more user-friendly.