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Limitations of Hadoop – How to overcome Hadoop drawbacks

@machinelearnbot

Let us start with what is Hadoop and what are Hadoop features that make it so popular. Hadoop is an open-source software framework for distributed storage and distributed processing of extremely large data sets. Important features of Hadoop are: Hadoop is an open source project. It means its code can be modified to business requirements. In Hadoop, data is highly available and accessible despite hardware failure due to multiple copies of data.


Hadoop vs. Spark – An Accurate Question? techsocialnetwork

#artificialintelligence

I just googled Hadoop vs. Spark and got nearly 35 million results. That's because Hadoop and Spark are two of the most prominent distributed systems for processing data on the market today. It's a hot subject that organizations are interested in when addressing their big data analytics. Choosing the Right Big Data Software; Which is the best Big Data Framework?; How Do Hadoop and Spark Stack Up?


Top Big Data Processing Frameworks

@machinelearnbot

With the modern world's unrelenting deluge of data, settling on the exact sizes which make data "big" is somewhat futile, with practical processing needs trumping the imposition of theoretical bounds. Like the term Artificial Intelligence, Big Data is a moving target; just as the expectations of AI of decades ago have largely been met and are no longer referred to as AI, today's Big Data is tomorrow's "that's cute," owing to the exponential growth in the data that we, as a society, are creating, keeping, and wanting to process. As such, traditional data processing tools which do not scale to big data will eventually become obsolete. So the question is, what are we doing with this data? The answer, of course, is very context-dependent.


Top Big Data Processing Frameworks

@machinelearnbot

With the modern world's unrelenting deluge of data, settling on the exact sizes which make data "big" is somewhat futile, with practical processing needs trumping the imposition of theoretical bounds. Like the term Artificial Intelligence, Big Data is a moving target; just as the expectations of AI of decades ago have largely been met and are no longer referred to as AI, today's Big Data is tomorrow's "that's cute," owing to the exponential growth in the data that we, as a society, are creating, keeping, and wanting to process. As such, traditional data processing tools which do not scale to big data will eventually become obsolete. So the question is, what are we doing with this data? The answer, of course, is very context-dependent.


The Lord of the Things: Spark or Hadoop?

@machinelearnbot

Are people in your data analytics organization contemplating the impending data avalanche from the internet of things and thus asking this question: "Spark or Hadoop?" The internet of things (IOT) will generate massive quantities of data. In most cases, these will be streaming data from ubiquitous sensors and devices. Often, we will need to make real-time (or near real-time) decisions based off of this tsunami of data inputs. How will we efficiently manage all of this, make effective use of it, and become lord over it before it becomes lord over us?