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What are big data's processing techniques?

2025-02-22 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > Development >

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Today, the editor will take you to understand the processing techniques of big data. The knowledge points in the article are introduced in great detail. Friends who feel helpful can browse the content of the article with the editor, hoping to help more friends who want to solve this problem to find the answer to the problem. Let's follow the editor to learn more about "what are big data's processing techniques?"

Big data has become a hot keyword in the domestic Internet field. With the continuous application of big data technology, a large number of new technologies have emerged, of which the very core technology is big data processing technology. Big data's processing techniques include: big data's collection, big data's preprocessing, big data village storage management, big data's analysis, big data's visualization and so on.

Big data's collection technology

Big data acquisition is the process of obtaining big data through a variety of data crawling and collection techniques. This process must pay attention to the identification, perception, adaptation, transmission, access and other technologies for large data sources. Generally speaking, the data acquisition method can be divided into system log acquisition method and network data acquisition method. Among them, the more commonly used is the web crawler in the network data acquisition method. The most common technology is the Python programming language.

Big data pretreatment technology

The main purpose of big data's preprocessing technology is to distinguish, extract and clear the collected data. The process of data extraction can help us to transform complex data into a single or easy-to-handle mechanism, while data cleaning is to remove the data content that we do not care about, so as to realize the "denoising" of data and improve the accuracy of data analysis.

Big data Storage and Management Technology

Big data needs to establish the corresponding database, manage and invoke it. This link requires the use of SQL-related technical knowledge. The main purpose is to solve several core problems of big data, such as storability, representability, course processing, reliability and effective transmission.

Big data's analytical techniques

Big data analysis is the core link of data analysis, and it is the process of realizing the data value through a variety of analysis means. Compared with the original small amount of data can be completed using Excel, in the face of big data's huge amount of data, we need to adopt more technologies to achieve.

Big data Visualization

Both Excel and Python programming can achieve the final visualization of the data. Make the data more intuitive. It is also the embodiment of the final result of data analysis.

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