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Erschienen in:
A Survey on Big Data—Its Challenges and Solution from Vendors Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Big Data Analysis in Cloud and Machine Learning

verfasst von : Neha Sharma, Madhavi Shamkuwar

Erschienen in: Big Data Processing Using Spark in Cloud

Verlag: Springer Singapore

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Abstract

In today’s digital universe, the amount of digital data that exists is growing at an exponential rate. Data is considered to be the lifeblood for any business organization, as it is the data that streams into actionable insights of businesses. The data available with the organizations are so much in volume that it is popularly referred as big data. It is the hottest buzzword spanning the business and technology worlds. Economies over the world is using big data and big data analytics as a new frontier for business so as to plan smarter business moves, improve productivity, improve performance, and plan strategy more effectively. To make big data analytics effective, storage technologies, and analytical tools play a critical role. However, it is evident that big data places rigorous demands on networks, storage and servers, which has motivated organizations and enterprises to move on cloud, in order to harvest maximum benefits of the available big data. Furthermore, we are also aware that conventional analytics tools are incapable to capture the full value of big data. Hence, machine learning seems to be an ideal solution for exploiting the opportunities hidden in big data. In this chapter, we shall discuss big data and big data analytics with a special focus in cloud computing and machine learning.

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Vorheriges Kapitel Big Data Streaming with Spark
Nächstes Kapitel Cloud Computing Based Knowledge Mapping Between Existing and Possible Academic Innovations—An Indian Techno-Educational Context
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Metadaten
Titel
Big Data Analysis in Cloud and Machine Learning
verfasst von
Neha Sharma
Madhavi Shamkuwar
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Big Data Processing Using Spark in Cloud

Print ISBN: 978-981-13-0549-8

Electronic ISBN: 978-981-13-0550-4

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-13-0550-4_3