MySQL Analytics Service: Tutorial, Pros & Cons

Nicholas Samuel • Last Modified: December 29th, 2022



MySQL is a popular database management system in many companies. Its wide adoption can be attributed to its simplicity and ease of use. MySQL organizes data into databases and tables, making it easy for enterprises to store and query their data. After storing data in MySQL, you will often need to extract insights from it. 

These insights can then be used to make sound decisions regarding the running and management of business activities. The good news is that MySQL provides you with the right tools for this. You can use its in-built tools to analyze your data and extract meaningful insights from it. 

In this article, we will be discussing the MySQL Analytics Service in detail. 


This is what you need for this article:

  • MySQL

Table of Contents

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Part 1: What is MySQL?

MySQL is a Relational Database Management System (RDBMS) that uses Structured Query Language (SQL). It is freely available, meaning that you can use it without paying anything. It is also open-source, meaning that you can change its source code to suit the needs of your business. 

Although MySQL is open-source software, you can buy its commercial license from Oracle to be receiving premium support services from them. 

It is very easy to master MySQL compared to other database management systems like Microsoft SQL Server and Oracle. MySQL can be installed on various platforms including Windows, UNIX, and Linux. It is fast, reliable, and scalable. 

You can run SQL commands on the MySQL terminal to insert, manipulate, and query data from it. 

Part 2: Data Analytics with MySQL

In this section, we will be discussing how to use SQL to query your MySQL relational database to extract insights from it. 

Querying a Relational Database

You can use the SQL SELECT command to query data from your relational database. 

The command takes the following syntax:

SELECT columns 
FROM table_name
WHERE condition;
  • The columns parameter denotes the table columns that are to be returned. 
  • The table_name parameter is the name of the table from which data is to be queried. 
  • The condition parameter is the condition that a row MUST meet so as to be returned.

For example, 

Let’s select all columns from a table named books2:

SELECT * FROM books2;

The command returns the following output:

Select all columns

If you need to select only specific columns/attributes from the table, just specify them as follows:

SELECT Bookid, Topic, Price from books2;
Select only specific columns

We can use a WHERE clause to specify a condition that must be met. 

For example:

SELECT * FROM books2
WHERE Topic = "IP";
Where clause

The command returns all records in the table where the topic is IP. 

We can also use the ORDER BY clause to order the results differently. 

Consider the following command:

SELECT * FROM books2
Using order by

We have used the Price column to order the results. 

By default, MySQL will order the results in ascending order. If you need to order the results in descending order, use DESC:

SELECT * FROM books2
Order by DESC

AND, OR and NOT operators

You can use the AND and OR operators within WHERE clauses. 

In MySQL, these two operators work in the same way that they work in other programming languages. 

The following command demonstrates how to use the AND clause:

FROM books2
WHERE Primary_author= 'Navathe' AND Topic = 'ADMT';
AND operator

When using the AND operator, the record to be returned must satisfy both conditions. 

So, only records in which the Primary_author is Navathe and the Topic is ADMT were returned. 

The same command can be written using the OR operator as follows:

FROM books2
WHERE Primary_author= 'Navathe' OR Topic = 'ADMT';
OR operator

When using the OR operator, the records to be returned must meet at least one of the conditions. So, only records in which the Primary_author is Navathe or the Topic is ADMT were returned. 

You can also add the NOT operator before the condition, and it will exclude all records that meet that condition. 

For example:

FROM books2
WHERE NOT Primary_author= 'Navathe' OR Topic = 'ADMT';
NOT operator

The NOT operator is a good operator when you need to exclude some records. 

Comparison Operators

You can also use comparison operators in your WHERE clauses. 

Here is the list of comparison operators supported in MySQL:

  • Less than: <
  • Less than or equal to: <=
  • Equal to: =
  • Not equal to: <> or !=
  • Greater than: >
  • Greater than or equal to: >=

For example, let us see some attributes for books whose price is below 50:

SELECT Primary_author, Topic, Price
FROM books2
WHERE Price < 50;
Comparator operator

The BETWEEN Operator

You can use the BETWEEN operator to select records with a value that is within a particular range. 

For example, let’s see the details of books whose price is between 50 and 100:

FROM books2
Between operator

Other than integer values, you can use the BETWEEN operator to find values belonging to other data types. 

The LIKE Operator

This operator allows us to search for values that match a particular pattern. 

You can create patterns using the following two wildcards:

  • %- it stands for zero, one, or multiple characters. 
  • _- it represents exactly one character. 

If you have used regular expressions before, you will find it easy to use this operator. 

FROM books2
WHERE Primary_author LIKE "%nkl%";
Like operator

In the above example, we are searching for the occurrence of the “nkl” pattern in the column named Primary_author

The % symbol added to the beginning and the end of the pattern will match any number of characters before and after the pattern. 

The IN Operator

This operator helps us avoid using many OR conditions, but instead gives MySQL the list of values that we want to use. 

For example:

FROM books2 
WHERE Primary_author IN ('Judith', 'David', 'Kaytlin', 'Korth');
IN operator

The command helped us look for records in the database whose Primary author is in the provided list.  

Finding Average 

During data analysis, you will normally want to find some aggregate values. 

The average is a good example of an aggregate value for data. 

MySQL provides the AVG() aggregator that you can use to find the average of your data. 

For example, to know the average price for the books, you can run the following command:

SELECT avg(Price)
FROM books2;


You can use the COUNT() function to count the instances of a certain value in a table. 

For example, to know the total number of records in a table, simply run the following command:

FROM books2;
Count column function

You can also make the count be more specific:

FROM books2
WHERE Topic = "ADMT";
Count column function

We have counted the number of records whose value for the Topic attribute is ADMT. 

Part 3: Pros

The following are the benefits of using MySQL for analytics:

  1. It is a good tool for analyzing relational data. 
  2. MySQL provides simple and easy-to-understand tools for data analytics. This makes it a good tool for beginners to data analytics. 

Part 4: Cons

The following are challenges of performing data analytics with MySQL:

  1. Not good for volatile data

If thousands of updates per second are made to a single database row, it will be very important to maintain exact values at every second. 

MySQL is designed for long transactions rather than highly volatile data. 

  1. Scalability issues

MySQL was designed to run as a single-node system and without the modern data center concept in mind. 

Due to this, it has scalability issues, hence, it’s not good for big data storage and analysis. 

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In this article, you’ve learned what MySQL is and how to use MySQL for Data Analytics. It also discusses the advantages and disadvantages of leveraging MySQL for Data Analytics in your workflow.

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