Data analysis is a key link in big data processing and application. It determines the value and availability of big data collections, as well as the accuracy of analysis and prediction results. In the data analysis process, appropriate data analysis technology should be selected based on big data application scenarios and decision-making needs to improve the usability, value, accuracy and quality of big data analysis results. What is the big data processing process?
big data processing process
1. Collection: The collection of big data refers to the use of multiple databases to receive data from clients (Web, App or sensor form, etc.), and users can perform simple queries and processing through these databases. In the process of collecting big data, its main feature and challenge is the high number of concurrency, because thousands of users may access and operate at the same time, so a large number of databases need to be deployed on the collection side to support it. And how to perform load balancing and sharding among these databases does require in-depth thinking and design.
2. Import/preprocessing: Although the collection end itself will have many databases, if you want to effectively analyze these massive data, you should import the data from the front end into a centralized large-scale distributed database, or distributed storage cluster, and can do some simple cleaning and preprocessing work based on the import.
3. Statistics/Analysis: Statistics and analysis mainly use distributed databases or distributed computing clusters to perform ordinary analysis, classification and summary of the massive data stored in them to meet most common analysis needs.
4. Mining: Different from the previous statistics and analysis process, data mining generally does not have any preset themes. It mainly performs calculations based on various algorithms on existing data to achieve a prediction effect. , thereby realizing some high-level data analysis requirements.
Features of big data center solutions
1. Reliability: It has industrial-grade features, including anti-electromagnetic compatibility, wide-temperature operation, millisecond-level network recovery, and can work in harsh environments.
2. Flexibility: Has multiple installation options and supports user-friendly features including simple control logic and Ethernet cascading.
3. Simplicity: Supports protocol conversion, unified data collection, and easily realizes data transmission and communication of various industrial equipment.
Big data solutions are very important in today's era. Only by understanding the types of data solutions, understanding their characteristics, finding solutions suitable for our own enterprises, and conducting data collection and analysis can we dig out the information we want. . Therefore, in the information age, if you want to expand your business development, you cannot ignore big data solutions.