MySQL and Snowflake are two popular tools in the data management landscape. Both storage systems have unique use cases, features, and functionalities. MySQL is a database that stores data in a structured manner. On the other hand, Snowflake is a storage system that can process large datasets in real time.

There are many situations when you want to load data from MySQL to Snowflake, as it provides several features that MySQL doesn't. These features include advanced analytical capabilities, real-time data processing, complex query execution, and extensive data warehousing functionalities.

This article will discuss two methods to perform MySQL to Snowflake migration.

MySQL Overview

Created by Oracle, MySQL is an open-source relational database management system. MySQL uses Structured Query Language (SQL) for querying databases, which makes it accessible and provides robust data manipulation, indexing, and querying capabilities. Its structured storage feature makes it ideal for organizations that require high-level data integrity, consistency, and reliability. Some major organizations that use MySQL include Amazon, Uber, Airbnb, and Shopify.

Key features of MySQL include: 

• Free to Use: As MySQL is open-source in nature, you can download, install, and start using MySQL without licensing costs. This allows you to leverage all the functionalities of a robust database management system without many barriers. However, for large organizations, it also provides commercial versions like MySQL Cluster Carrier Grade Edition and MySQL Enterprise Edition. ‍
• Diverse Storage Engines: Storage engines are MySQL components that handle SQL queries and operations for different table types. MySQL provides many storage engines, including InnoDB MyISAM, MEMORY, ARCHIVE, CSV, and BDB, each offering unique capabilities and serving different database needs. Of all different storage engines, MySQL uses InnoDB by default and recommends using it for tables except for specific use cases. 

Snowflake Overview

Snowflake is a unified software platform that provides different tools for many data management services in one place. These services include data analytics, data lake, engineering, and warehousing. However, it is widely known for its data warehousing services. Its cloud-based storage system leverages a shared architecture that allows you to store and analyze huge datasets in real-time. Some major organizations that use Snowflake on their tech stack include Sony, Adobe, and Capital One.

Key features of Snowflake include: 

• Storage And Compute Separation: The cloud-native architecture of Snowflake separates storage and computing resources. This feature enables you to independently scale each, which results in performance optimization and cost management. 
• Diverse Data Format Support: Snowflake allows you to handle both structured and semi-structured data within the same storage system. This includes formats like JSON, Avro, XML, and Parquet. Using this feature, you can reduce the need for pre-processing or transforming data before loading it to the destination.

Methods to Perform MySQL to Snowflake Data Replication

• Method 1: Using Airbyte to Connect MySQL to Snowflake.‍
• Method 2: Replicating MySQL Data to Snowflake Using CSV Files.

Method 1: Using Airbyte to Connect MySQL to Snowflake

Airbyte is like a data engineer's secret weapon! With its powerful capabilities, you can set up various Data Integrations including MySQL to BigQuery and MySQL to Redshift, among many other connections. It's the perfect tool to supercharge your data engineering projects and make them shine! Here is a detailed guide: 

Step 1: Set Up MySQL As a Source

• Sign up or log in to the Airbyte cloud platform. 
• On the home page of Airbyte cloud, click on the Sources tab from the left navigation bar. 
• On the Sources page, locate the Search field and enter MySQL. When the MySQL connector card displays, click on it. 

• You will be directed to the Create a source page. Fill in details in fields including Host, Port, Database, Username, Password, and SSL modes. 
• Scroll to the Update Method section and select between the following options: Read Changes using Binary Log (CDC) (recommended method by Airbyte), Scan Changes with User-Defined Cursor.

• Optionally, fill in other fields and click on Set up source.

Step 2: Step Up Snowflake As Destination 

• After configuring MySQL as the source, click on the Destinations tab from the left navigation bar.
• On the Destinations page, type in Snowflake in the Search field and click on the connector card that appears. 

• You'll be directed to Create a destination page. Fill in the fields: Host, Role, Warehouse, Database, Default_Schema, and Username. 
• Select the appropriate Authorization Method between OAuth2.0, Key Pair Authentication, Username, and Password. 

• Click on Set up destination. 

Step 3: Create a Connection Between MySQL And Snowflake

• Now that you have configured a source and destination in the Airbyte cloud, you must establish a connection between the two. You can simply click the Connections tab on the navigation bar or select the Create a new connection option after creating the destination. 

• Click on MySQL from Step 1 as the source and Snowflake from Step 2 as the destination. 
• Configure connection details on the Create a connection page and provide a unique Connection Name. You can tweak details, including sync mode, Streams section, and Replication frequency. 
• Click on Set up connection and then sync now to start synchronizing data between the source and destination. 

Done. You have successfully connected MySQL and Snowflake using Airbyte. 

Method 2: Replicating MySQL Data to Snowflake Using CSV Files

In this method, you will learn to migrate MySQL data to the Snowflake warehouse, focusing on one table at a time using CSV files. Here's a detailed guide: 

Prerequisites

• MySQL Server. 
• Access to Snowflake web interface. 

Step 1: Export MySQL Data in a CSV Files

First, you have to access the MySQL. For this, launch the terminal and run the following command:

mysql -u username -p

Replace the username field with MySQL username and give in the password. 

Navigate to the specific database you want to export the data to Snowflake. Type in this code:

use your_database;

Replace your_database with your database name.

Now, access the specific MySQL table data in the database and export it in CSV file format in your provided location. To perform this task, type in the following in the terminal:

SELECT column_name, column_name, column_name FROM your_table
INTO OUTFILE 'your_location/mysql_data.csv' 
FIELDS TERMINATED BY ',' 
ENCLOSED BY '"' 
LINES TERMINATED BY '\n';

The above code selects a MySQL database table (your_table) and its different fields (column_name), exports it using the INTO OUTFILE command in your provided location (your_location) and CSV file name (mysql_data.csv).

Ensure the above steps are carried out carefully and verify if the mysql_data.csv file is saved in your given location.

Step 2: Import the CSV File into Snowflake Web Interface

• Login to Snowsight. 
• On the home page, select Data > Databases from the navigation bar.

• Select the database and table in which you want to import the data. 
• Click Load Data > Load Data into Table > Browse. 
• Select the warehouse if you do not have a warehouse set by default. 
• Click Next. 

• Now, you'll be in the Source Files section. Select the Load files from your computer option and add a CSV file.

• Configure details as per your requirement in the Load Options section and click Load. 
• If you want to open a worksheet with SQL syntax for table query, select Query Data or click Done.
• Snowsight will successfully load your CSV data file into the warehouse. 

Thats all. If you have carefully followed every step mentioned above, you can easily replicate data manually.

Limitations of Manually Migrating Data From MySQL to Snowflake

• Error-prone: The manual method is a long process requiring custom coding to move data from MySQL to Snowflake. This might involve tweaking data types and writing SQL queries, which increases the chance of human error and can lead to data loss and integrity. ‍
• Repetitive: Since this method extracts and imports MySQL data one table at a time, having multiple tables to extract data from can lead to repetition, which is very inefficient and time-consuming. 
• ‍Orchestration Challenges: Unlike automated solutions like Airbyte, the manual method lacks orchestration capabilities like monitoring data integration progress and alerts for issues during data transfer.

Conclusion

You have now learned two straightforward methods to load data from MySQL to Snowflake. The first method uses Airbyte to automate the connection between both storage systems. All you need to do is, access the platform, do a few clicks, as mentioned above, and the connection between MySQL and Snowflake will succeed.

However, the second method involves a manual effort to complete the same task. First, you must access the MySQL database, export the data in a CSV file, and then import it to the Snowflake warehouse.

In both methods, we suggest using the first one, i.e., Airbyte, to streamline the data migration between both storage systems. Integrating data from MySQL to Snowflake brings a whole bunch of benefits to the table. And when you utilise one of the powerful data integration tools like Airbyte to the mix, it's like adding a whole new set of feathers to its cap! If you're interested in optimizing your data synchronization process, check out our tutorial on Snowflake CDC for in-depth insights.

You can also checkout another helpful article that takes you through the step-by-step process of connecting Postgres to Snowflake.