Often, there is a need to migrate data from MySQL to external sources, which may be to a file (CSV, Excel), another database, a cloud platform, or some other external tool.
The movement of data is required when you want to perform analytics on the data (which may not be possible in MySQL itself) or to move the data into a modern ecosystem for better compatibility.
This article will give you a comprehensive guide on MySQL and its key features in brief. You will also explore various methods to export data from MySQL.
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What is MySQL?
MySQL is an accessible and popular open-source Relational Database. It is widely used by many small and big industries when there is a need to store structured data and perform quick analytics to understand the behavior of the data.
Key Features of MySQL
Some of the most important key features of MySQL are as follows:
- Free Source: Free and easy to use.
- ANSI SQL: MySQL uses the standard form known as ANSI SQL with a universe of Data Analytical functions.
- Driver availability: With the help of available drivers, it can easily integrate with many programming languages and modern tools.
- Integration: Easy integration with operating systems.
- Support huge databases: MySQL provides support for large databases, up to 50 million rows or more in a table.
- Memory Usage: The default file size limit for a table is 4GB, but this can be increased to a limit of 8 million terabytes (TB) (provided the operating system supports it).
To know more about MySQL.
Why exporting a database is important?
- Back up for catastrophic system failure: Exporting a database creates a backup that you can use to restore your database in case of system failure.
- Data versioning: Specific versions on your database can be saved by exporting the database from MySQL. You can analyze or restore any particular version of the database.
Read more about MySQL Workbench Database Dump.
Prerequisites
- MySQL Server running on-premise or on the cloud.
- Basic knowledge of Data Export MySQL Workbench feature.
Note: This article uses MySQL 8.0.20 to demonstrate the Data Export MySQL Workbench feature.
Key Considerations Before Exporting Data
- The schema we have defined is “test”.
- The table name is “orders”.
- The orders contain the following attributes:
- order_id: Id of the order placed.
- order_date: Date of the order placed.
- custormer_id: Id of the customer who has placed the order.
- order_status: Status of the order (CLOSED, PENDING_PAYMENT, COMPLETE, PROCESSING, PAYMENT_REVIEW, PENDING, ON_HOLD, CANCELED, SUSPECTED_FRAUD).
- The MySQL Server’s process should have write access to the target folder containing the target CSV file.
- The target CSV file must not exist.
Method 1: Data Export MySQL Workbench Feature
The Data Export MySQL Workbench feature provides an inbuilt option to export the data into CSV format.
You can download Data Export MySQL Workbench from here. Below is the step-by-step procedure on how you can utilize the Data Export MySQL Workbench feature with ease.
- First, prepare the query and execute it to get the desired result set.
- From the result panel, click on the Export option.
- On the save dialog box, enter the file name, choose CSV as the file format, and click the Save button as shown by the image below.
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Method 2: Using MySQL Commands
The simple way to export data from MySQL is to use the inbuilt feature of extracting the data by using the SQL approach. This command exports the data into CSV format with the specified “delimiter” and “escape characters”.
CSV files are the best when we talk about Data Analytics because of their structured format and ease of integration with the existing available tools.
Step 1: First, we have to prepare the query which contains the desired data to be exported. Here, we are exporting the data where order_status is COMPLETED.
Step 2: Mention the optional parameters like field delimiter, field enclosing option, and execute the query using execute option in the Workbench.
- Step 3: On successful completion of the query, the CSV will be exported to the mentioned location.
Below are the schematics for the MySQL command :
Below is the command for your reference :
SELECT order_id, order_date, customer_id, order_status
FROM test.orders
WHERE order_status = 'COMPLETE'
INTO OUTFILE 'c:/Temp/output/Completed/orders.csv'
FIELDS ENCLOSED BY ''
TERMINATED BY ','
ESCAPED BY '"'
LINES TERMINATED BY '
';
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Additional Resources on Data Export MYSQL Workbench
Conclusion
- There are a variety of options in the market to migrate or export data from the MySQL server to the required destination.
- One of the popular in-house options among them is the Data Export MySQL Workbench feature which has been discussed in this article.
- Now, you have a comprehensive idea of how you can easily export data using the Data Export MySQL Workbench and SQL commands.
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Have you used the Data Export MySQL Workbench feature? Let us know in the comments section below.
FAQs
1. How do I extract data from Workbench?
In MySQL Workbench, just right-click on the table you want to get data from and choose “Select Rows.” Once you see the data, you can export it by clicking the “Export” button at the bottom.
2. How do I export all tables in MySQL Workbench?
Go to Server > Data Export in MySQL Workbench. Select the database, and then pick whether you want to export just the data or both structure and data. You can then save it as a file, like CSV or SQL.
3. How do you export data from a table in MySQL?
Right-click on the table you want to export in MySQL Workbench, select “Table Data Export Wizard,” choose the file format you want (like CSV), and follow the steps to save it.
Vishal Agarwal is a Data Engineer with 10+ years of experience in the data field. He has designed scalable and efficient data solutions, and his expertise lies in AWS, Azure, Spark, GCP, SQL, Python, and other related technologies. By combining his passion for writing and the knowledge he has acquired over the years, he wishes to help data practitioners solve the day-to-day challenges they face in data engineering. In his article, Vishal applies his analytical thinking and problem-solving approaches to untangle the intricacies of data integration and analysis.