Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs.
Hadoop [1] is the most mature open-source big data analytics framework, which implements the MapReduce programming model [2] proposed by Google in 2004 to process big data.
From the perspective of big data analytics framework and platform, the discussions are focused on the performance-oriented and results-oriented issues.
To discuss in deep the big data analytics, this paper gives not only a systematic description of traditional large-scale data analytics but also a detailed discussion about the differences between data and big data analytics framework for the data scientists or researchers to focus on the big data analytics.
But when we enter the age of big data, most of the current computer systems will not be able to handle the whole dataset all at once; thus, how to design a good data analytics framework or platform3 and how to design analysis methods are both important things for the data analysis process.
The unprecedented volume of data currently being processed by data analytics frameworks requires scalable machine learning algorithms.
Enterprise architecture is expanding to include the communications network of the internet of things, and data from wearable devices is being incorporated in big data analytics frameworks.
In addition, we analyze and classify the state-of-the-art of big data analytics frameworks, available today mostly on Clouds, based on their supported service models.
However, we have shown that with little effort, by using intuitive concepts, an offline analytics application can obtain the required data, which can then be analyzed with data analytics frameworks, e.g., MapReduce.
Since many kinds of data analytics frameworks and platforms have been presented, some of the studies attempted to compare them to give a guidance to choose the applicable frameworks or platforms for relevant works.
