Shulou(Shulou.com)06/03 Report--

This article will explain in detail who first put forward the current technical basis of big data. The content of the article is of high quality, so the editor will share it with you for reference. I hope you will have a certain understanding of the relevant knowledge after reading this article.

At present, the basis of big data's technology was first put forward by Google.

Big data (big data), IT industry term, refers to the data set that can not be captured, managed and processed with conventional software tools within a certain period of time. It is a massive, high growth rate and diversified information asset that requires a new processing model to have stronger decision-making power, insight and process optimization ability.

In the big data era, written by Victor Mayr-Schoenberg and Kenneth Cukeye, big data means that all data are analyzed instead of shortcuts such as random analysis (sampling). Big data's 5V features (proposed by IBM): Volume (mass), Velocity (high speed), Variety (diversity), Value (low value density), Veracity (authenticity).

This definition is given for "big data" (Gartner), a research institution of "big data". "big data" needs a new processing model to have stronger decision-making power, insight and process optimization ability to adapt to massive, high growth rate and diversified information assets.

The definition given by the McKinsey Global Research Institute is that the scale of data collection is so large that it is far beyond the capabilities of traditional database software tools in terms of acquisition, storage, management and analysis. it has four characteristics: massive data scale, rapid data flow, diverse data types and low value density.

The strategic significance of big data's technology is not to master huge data and information, but to deal with these meaningful data professionally. In other words, if big data is compared to an industry, the key to profitability of this industry lies in improving the "processing ability" of data and realizing the "value-added" of data through "processing".

Technically, the relationship between big data and cloud computing is as inseparable as the pros and cons of a coin. Big data must not be able to process with a single computer and must adopt a distributed architecture. It is characterized by distributed data mining for massive data. But it must rely on the distributed processing, distributed database, cloud storage and virtualization technology of cloud computing.

With the advent of the cloud era, big data (Big data) has also attracted more and more attention. According to the team of analysts, big data is often used to describe the large amount of unstructured and semi-structured data created by a company that takes too much time and money to download to a relational database for analysis. Big data analysis is often associated with cloud computing because real-time large dataset analysis requires a framework like MapReduce to assign work to dozens, hundreds or even thousands of computers.

Big data needs special techniques to effectively handle large amounts of tolerated data over a period of time. The technologies applicable to big data include massively parallel processing (MPP) databases, data mining, distributed file systems, distributed databases, cloud computing platforms, the Internet and scalable storage systems.

As to who first put forward the current technical basis of big data, I hope the above content can be of some help to you and learn more knowledge. If you think the article is good, you can share it for more people to see.

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