How to load data from MySQL to PostgreSQL?: 2 Easy Ways
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Before moving forward with the migration process, a clear understanding of the changes required to work with Postgres is needed. In this step, you must create a new schema in the PostgreSQL database and match the structure of the MySQL database, Since each database uses its own data types, storage formats, and SQL syntax, this step is very crucial. You can choose to do schema conversion manually by tweaking data types, as both databases have partially similar structures. However, you can also use libraries such as DBConvert to do this task for you.
Open the terminal and navigate to the directory where MySQL is installed. Type in the following command:
mysql -u username -p
Replace username with MySQL username.
Now, use the mysqldump command to export the MySQL database into a file. Type this command to perform this task:
mysqldump -u username -p your_database > mysql_data.sql
Replace username with MySQL username, your_database with the name of MySQL database, and mysql_data.sql with the file name in which you want to store exported data.
Note: You can also choose to export specific tables and objects from the MySQL database using the mysqldump command.
After you get the file, type in mysql exit and close the terminal.
Open the terminal and go to the Postgres directory. Run the following code to access the database:
psql -U username -d your_database
From the above code, replace username with your Postgres username and your_database with the database name you want to access.
Now you are connected, you must import the data of the MySQL database to Postgres. Run the following code to achieve this task:
psql -U username -d your_database -f mysql_data.sql
The above command will connect to a specific database (your_database) as a specified user (username) and execute the SQL commands contained in the mysql_data file while importing the data.
Note: The schema in your Postgres database should be compatible with data in mysql_data.sql file. Therefore, make sure Step 1 is carried out carefully.
That concludes it. If you have followed every step of the manual method, you can migrate data from MySQL to Postgres without difficulty.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
MySQL is an SQL (Structured Query Language)-based open-source database management system. An application with many uses, it offers a variety of products, from free MySQL downloads of the most recent iteration to support packages with full service support at the enterprise level. The MySQL server, while most often used as a web database, also supports e-commerce and data warehousing applications and more.
MySQL provides access to a wide range of data types, including:
1. Numeric data types: These include integers, decimals, and floating-point numbers.
2. String data types: These include character strings, binary strings, and text strings.
3. Date and time data types: These include date, time, datetime, and timestamp.
4. Boolean data types: These include true/false or yes/no values.
5. Spatial data types: These include points, lines, polygons, and other geometric shapes.
6. Large object data types: These include binary large objects (BLOBs) and character large objects (CLOBs).
7. Collection data types: These include arrays, sets, and maps.
8. User-defined data types: These are custom data types created by the user.
Overall, MySQL's API provides access to a wide range of data types, making it a versatile tool for managing and manipulating data in a variety of applications.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
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