MySql's analytical database: How to use MySQL to implement big data analysis

With the rapid development of the Internet and mobile Internet, the explosive growth of data volume has become a norm. How to quickly analyze large amounts of data to obtain valuable information has become an urgent need for businesses and individuals. In this regard, MySql's analytical database came into being. This article will introduce how to use MySQL to implement big data analysis.

1. What is an analytical database

In the past, traditional databases were mainly based on transaction processing, that is to say, operations such as modification, addition, deletion, and query of a certain row of records. If the size of the database is small, there are not many data tables, and the transaction volume is not large, then this traditional database is capable. But as the scale gradually expands, the performance of this database becomes a bottleneck. This is where an analytical database is needed. Analytical database (OLAP) is mainly focused on data analysis, and is aimed at operations such as complex calculations and application analysis of multi-row records.

2. Why choose MySql

MySQL is a database with good scalability and is suitable for distributed databases. Moreover, MySQL also provides some high-availability functions such as partitioning and replication to make data more available. At the same time, MySQL's powerful functions in data storage, calculation and analysis are the first choice for analytical databases.

3. How to use MySql to implement big data analysis

1. Data preparation

First of all, you need to be able to obtain a large amount of data. The larger the data size, the greater the value of the data. The best way is to import data from different sources into MySQL to form integrated data processing to facilitate data management and unified operations.

2. Data analysis

Data analysis is the core of the entire big data analysis process. In MySql, data analysis can be easily achieved by using efficient analysis tools and algorithms.

①Use Group By for data aggregation and summary

Group By is the most commonly used data aggregation and summary tool for MySql. Group By can be used to sort and summarize data according to specified conditions. The specifics can be as follows:

SELECT COUNT(*) AS cnt, name FROM tbl GROUP BY name ORDER BY cnt DESC

Take the above command as an example to classify and count the names and perform statistics according to the quantity Sort in reverse order.

②Use Union to realize data merging

Union is also a very commonly used data merging command in MySql. It can merge different data tables together to facilitate data processing and analysis. The details can be as follows:

SELECT name FROM tbl1 UNION SELECT name FROM tbl2

Take the above command as an example to merge the data with the same name in the two data tables tbl1 and tbl2. And remove the duplicates.

③Use Join for data connection

Join is also a very commonly used data connection command in MySql. It can connect different data tables together to facilitate more complex data analysis and processing. The details can be as follows:

SELECT t1.id, t1.name, t2.salary FROM tbl1 t1 JOIN tbl2 t2 ON t1.id=t2.id

Take the above command as an example, Connect the data with the same ID in the two data tables tbl1 and tbl2, and sort them according to the ID number.

④Use subquery for data analysis

Subquery is also a very commonly used data analysis command in MySql. It can contain another query in a query statement. The details can be as follows:

SELECT name FROM tbl WHERE id IN (SELECT id FROM tbl2 WHERE salary>10000)

Take the above command as an example to query the tbl table whose salary is greater than 10,000 yuan Employee's name.

4. Data Visualization

Data visualization is one of the important aspects of big data analysis. MySQL supports a variety of data visualization tools and languages, such as PowerBI, Excel, Python, etc. While realizing data visualization, it can also further process and analyze the data. Data visualization can not only help analysts better understand and recognize the data, but also show the internal correlation and trend development of the data.

In short, how to use MySQL to implement big data analysis requires aggregating, summarizing and connecting a large amount of data, and working with data visualization tools and algorithms to conduct more effective data analysis and processing. At the same time, it is also necessary to select efficient MySql database and analysis tools to ensure data availability and computing efficiency.

The above is the detailed content of MySql's analytical database: How to use MySQL to implement big data analysis. For more information, please follow other related articles on the PHP Chinese website!