Author: Zen and the Art of Computer Programming
1 Introduction
The amount of data is getting larger and larger, application scenarios are getting more complex, and users have higher and higher requirements for data. People are gradually turning their attention to how to process massive data quickly and efficiently. Driven by new trend technologies such as artificial intelligence, cloud computing, and big data, the field of data analysis has transformed from traditional rule-based analysis to "intelligent" analysis. As artificial intelligence models become more and more accurate, energy consumption is reduced, memory usage is reduced, computing speed is increased, and data storage is reduced, we can foresee that artificial intelligence will become the main development direction, and data analysis will become a new leader. In order to better manage and process massive amounts of data, we need to transform and upgrade the architecture and build a data-oriented intelligent data processing platform. Based on the characteristics and development trends of big data and intelligent data applications, this article attempts to present a complete set of data analysis platform architecture designs to readers, and combined with actual cases, explains how to build a big data intelligent architecture in Internet companies.
2. Overview of data analysis
Data Analysis refers to discovering information from data according to rules or patterns, and making decisions, guiding actions or adjusting strategies accordingly. Generally speaking, data analysis methods include:
Empirical method: Based on past experience, the summary and understanding of personnel with a certain level of knowledge of the data. Such as market research, public policy, public opinion monitoring, etc.
Technical approach: Using techniques such as computer analysis, statistics, and mathematics to process data and identify patterns and trends. Such as data mining, business intelligence, image analysis, text mining, etc.
Theoretical method: use economics, physics, mathematics, psychology, philosophy, social science and other discipline theories to abstract, summarize and explain data. Such as history, macroeconomics, biology, cognitive science, etc.
Data analysis applications typically fall into the following categories:
Data Mining (Data Mining): Extracting valuable information from large amounts of data, building data models, discovering hidden relationships and patterns, through