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:
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:
- 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.
Let’s select all columns from a table named books2:
SELECT * FROM books2;
The command returns the following output:
If you need to select only specific columns/attributes from the table, just specify them as follows:
SELECT Bookid, Topic, Price from books2;
We can use a WHERE clause to specify a condition that must be met.
SELECT * FROM books2
WHERE Topic = "IP";
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
ORDER BY PRICE ASC;
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 PRICE 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:
WHERE Primary_author= 'Navathe' AND Topic = 'ADMT';
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:
WHERE Primary_author= 'Navathe' OR Topic = 'ADMT';
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.
WHERE NOT Primary_author= 'Navathe' OR Topic = 'ADMT';
The NOT operator is a good operator when you need to exclude some records.
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
WHERE Price < 50;
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:
WHERE Price BETWEEN 50 AND 100;
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.
WHERE Primary_author LIKE "%nkl%";
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.
WHERE Primary_author IN ('Judith', 'David', 'Kaytlin', 'Korth');
The command helped us look for records in the database whose Primary author is in the provided list.
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:
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:
You can also make the count be more specific:
WHERE Topic = "ADMT";
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:
- It is a good tool for analyzing relational data.
- 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:
- 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.
- 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.
Part 5: Use Hevo Data
Hevo Data provides its users with a simpler platform for integrating data for analysis.
It is a no-code data pipeline that can help you combine data from multiple sources.
It provides you with a consistent and reliable solution to managing data in real-time, ensuring that you always have analysis-ready data in your desired destination.
Your job will be to focus on key business needs and perform insightful analysis using BI tools. To try Hevo out, sign-up for a free trial here!
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.
Extracting complex data from a diverse set of data sources can be a challenging task and this is where Hevo saves the day! Hevo offers a faster way to move data from Databases or SaaS applications into your Data Warehouse to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code.
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Hevo Data will automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. This platform allows you to transfer data from 100+ multiple sources to Cloud-based Data Warehouses like Snowflake, Google BigQuery, Amazon Redshift, etc. It will provide you a hassle-free experience and make your work life much easier.
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