Practical examples of the use of Hadoop in Big Data

[shukla7789]

Companies have found a myriad of different uses for Hadoop to harness big data and gain a competitive advantage.
Not every company needs Big Data , but regardless of their size and sector, if they need to work with big data, it is safe to say that Hadoop will almost certainly be the most efficient framework to obtain the maximum benefits from data science.
The creation of this open source framework responded to a practical need that demanded a great solution to solve a big problem. Google found itself unable to meet the market demands when it came to indexing data and decided to look for a solution that eventually took the form of an effective distributed file system.

It was in 2006 when Google published the details of its revolutionary discovery , not only because it found an unprecedented solution that offered great possibilities at low cost, but because it would lead to the birth of Hadoop, which as an opensource framework in continuous development is offering great opportunities to organizations to store, process and analyze large volumes of data.

Its unstoppable popularity in production environments is due, among other reasons, to the minimization of application risks that are obtained thanks to the appearance of different commercial distributions that include technical support, so it is not necessary to depend on the open source community to solve possible problems.

Today, its enormous versatility is proving to have no moj database in terms of possible applications. Hadoop developers and data scientists have a universe to explore before them, a whole world of possibilities to achieve juicy competitive advantages by working with data.


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Hadoop for massive data analysis
Whether small or large-scale projects, to achieve one goal or another, organizations are adopting it to take advantage of its benefits , including its star option: queries of stored information that, thanks to its rich ecosystem, can also be performed in real time.

Indeed, any organization that uses Hadoop can take advantage of vast amounts of unstructured data from different sources. Its implementation goes beyond the simple storage of a huge amount of data, because although it is a centralized data repository, its analytical capacity is what really makes the difference as an open platform for different approaches or processing technologies.

Hadoop is not just a storage and processing framework. Above all, it is a transformative platform that adapts to different needs to provide a competitive advantage in a wide variety of aspects, from discovering new ideas, improving operational processes or, for example, helping to make informed decisions that are much more reliable than those based simply on experience or intuition.