From a technical point of view, the relationship between big data and cloud computing is as inextricably linked as the positive and negative sides of a coin. Inevitably, big data cannot be processed by a single computer, but must be distributed. It is characterized by distributed data mining of massive amounts of data, but it must rely on cloud computing's distributed processing, distributed databases and cloud storage, virtualization technology.
With the advent of the cloud era and the growing interest in big data, a team of analysts believes that big data is often used to describe a large amount of unstructured and semi-structured data created by a company. Big data analytics is often associated with cloud computing because real-time analysis of large data sets requires frameworks like MapReduce to distribute work to dozens, hundreds or even thousands of computers.
Big data requires special techniques to efficiently process large amounts of data over tolerable elapsed time. Technologies applicable to Big Data include massively parallel processing databases, data mining, distributed file systems, distributed data can, cloud computing platforms, the Internet, and scalable storage systems.
Now that we have entered the era of big data, Harvard sociology professor Gary King said, "It's a revolution, and the vast resources of data have enabled a quantitative process to begin in all fields, whether it's in academia, business or government, and it's going to begin that process in all fields."
The Internet of Things can't grow without big data, and relying on it provides a reliable resource, while big data also drives the Internet of Things. As an example: connecting sensors in cars and combining them with big data and analytics to predict when a car is likely to break down before it actually does. This process not only informs the driver, but that their vehicle could break down before service, which can support automakers in investigating potential defects and improving future models.