Relational databases, such as MySQL, have traditionally helped enterprises manage and analyze massive volumes of data effectively. However, as scalability, real-time analytics, and seamless data integration become increasingly important, contemporary data systems like Snowflake have become strong substitutes. After experimenting with a few different approaches and learning from my failures, I’m excited to share my tried-and-true techniques for moving data from MySQL to Snowflake.

In this blog, I’ll walk you through two simple migration techniques: manual and automated. I will also share the factors to consider while choosing the right approach. Select the approach that best meets your needs, and let’s get going!

What is MySQL?

MySQL is an open-source relational database management system (RDBMS) that allows users to access and manipulate databases using Structured Query Language (SQL). Created in the middle of the 1990s, MySQL’s stability, dependability, and user-friendliness have made it one of the most widely used databases worldwide. Its structured storage feature makes it ideal for organizations that require high-level data integrity, consistency, and reliability. Some significant organizations that use MySQL include Amazon, Uber, Airbnb, and Shopify.

Key Features of MySQL : 

  • Free to Use: MySQL is open-source, so that you can download, install, and use it without any licensing costs. This allows you to use all the functionalities a robust database management system provides without many barriers. However, for large organizations, it also offers commercial versions like MySQL Cluster Carrier Grade Edition and MySQL Enterprise Edition.
  • Scalability: Suitable for both small and large-scale applications.

What is Snowflake?

Snowflake is a cloud-based data warehousing platform designed for high performance and scalability. Unlike traditional databases, Snowflake is built on a cloud-native architecture, providing robust data storage, processing, and analytics capabilities.

Key Features of Snowflake :

  • Cloud-Native Architecture: Fully managed service that runs on cloud platforms like AWS, Azure, and Google Cloud.
  • Scalability and Elasticity: Automatically scales compute resources to handle varying workloads without manual intervention.

Why move MySQL data to Snowflake?

  • Performance and Scalability: MySQL may experience issues managing massive amounts of data and numerous user queries simultaneously as data quantity increases. Snowflake’s cloud-native architecture, which offers nearly limitless scalability and great performance, allows you to handle large datasets and intricate queries effectively.
  • Higher Level Analytics: Snowflake offers advanced analytical features like data science and machine learning workflow assistance. These features can give you deeper insights and promote data-driven decision-making. 
  • Economy of Cost: Because Snowflake separates computation and storage resources, you can optimize your expenses by only paying for what you utilize. The pay-as-you-go approach is more economical than the upkeep and expansion of MySQL servers situated on-site.
  • Data Integration and Sharing: Snowflake’s powerful data-sharing features make integrating and securely exchanging data easier across departments and external partners. This skill is valuable for firms seeking to establish a cohesive data environment.
  • Streamlined Upkeep: Snowflake removes the need for database administration duties, which include software patching, hardware provisioning, and backups. It is a fully managed service that enables you to concentrate less on maintenance and more on data analysis.

Methods to transfer data from MySQL to Snowflake:

Method 1: How to Connect MySQL to Snowflake using Custom Code


  • You should have a Snowflake Account. If you don’t have one, check out Snowflake and register for a trial account.
  • A MySQL server with your database. You can download it from MySQL’s official website if you don’t have one.

Let’s examine the step-by-step method for connecting MySQL to Snowflake using the MySQL Application Interface and Snowflake Web Interface. 

Step 1: Extract Data from MySQL

  • I created a dummy table called cricketers in MySQL for this demo. 
Dummy database
  • You can click on the rightmost table icon to view your table. 
  • Next, we need to save a .csv file of this table in our local storage to later load it into Snowflake.  
  • You can do this by clicking on the icon next to Export/Import. 
  • This will automatically save a .csv file of the table that is selected on your local storage.
Export/Import to save your .csv file

Step 2: Create a new Database in Snowflake

  • Now, we need to import this table into Snowflake. 
  • Log into your Snowflake account, click Data>Databases, and click the +Database icon on the right-side panel to create a new database. 
  • For this guide, I have already made a database called DEMO.
Dummy Database in Snowflake

Step 3:  Create a new Table in that database

  • Now click DEMO>PUBLIC>Tables, click the Create button, and select the From File option from the drop-down menu.
Drop down menu
  • A Dropbox will appear where you can drag and drop your .csv file. 
  • Select and create a new table and give it a name. 
  • You can also choose from existing tables, and your data will be appended to that table.

Step 4: Edit your table schema

  • Click next. In this dialogue box, you can edit the schema.
  • After modifying the schema according to your needs, click the load button. 
  • This will start loading your table data from the .csv file to Snowflake.
Load data into table

Step 5: Preview your loaded table 

  • Once the loading process has been completed, you can view your data by clicking the preview button.
Preview your loaded table

Note: An alternative method of moving data is to create an Internal/External stage in Snowflake and load data into it. 

Limitations of Manually Migrating Data from MySQL to Snowflake:

  • Error-prone: Custom coding and SQL Queries introduce a higher risk of errors potentially leading to data loss or corruption.
  • Time-Consuming: Handling tables for large datasets is highly time-consuming.
  • Orchestration Challenges: Manually migrating data needs more monitoring, alerting, and progress-tracking features.

Method 2: How to Connect MySQL to Snowflake using an Automated ETL Platform


  • To set up your pipeline, you need a Hevo account. If you don’t have one, you can visit Hevo.
  • A Snowflake account.
  • A MySQL server with your database.

Step 1:Connect your MySQL account to Hevo’s Platform.

  • To begin with, I am logging in to my Hevo platform. Next, create a new pipeline by clicking the Pipelines and the +Create button. 
Create your pipeline
Create your pipeline
  • Hevo provides built-in MySQL integration that can connect to your account within minutes. Choose MySQL as the source and fill in the necessary details.
  • Enter your Source details and click on TEST & CONTINUE.
Configure MySQL Source
Configure MySQL Source

Next, Select all the objects that you want to replicate. Objects are nothing but the tables.

Step 2: Connect your Snowflake account to Hevo’s Platform

Configure Snowflake Destination

You have successfully connected your source and destination with these two simple steps. From here, Hevo will take over and move your valuable data from MySQL to Snowflake. 

Sync your MySQL to Snowflake

Advantages of using Hevo:

  • Auto Schema Mapping: Hevo eliminates the tedious task of schema management. It automatically detects the schema of incoming data and maps it to the destination schema.
  • Incremental Data Load: Allows the transfer of modified data in real-time, ensuring efficient bandwidth utilization on both ends.
  • Data Transformation: It provides a simple interface for perfecting, modifying, and enriching the data you want to transfer.

Note: Alternatively, you can use SaaS ETL platforms like Estuary or Airbyte to migrate your data.

Best Practices for Data Migration:

  • Examine Data and Workloads: Before migrating, constantly evaluate the schema, volume of your data, and kinds of queries currently running in your MySQL databases.
  • Select the Appropriate Migration Technique:
    • Handled ETL Procedure: This procedure is appropriate for smaller datasets or situations requiring precise process control. It requires manually loading data into Snowflake after exporting it from MySQL (for example, using CSV files).
    • Using Snowflake’s Staging: For larger datasets, consider utilizing either the internal or external stages of Snowflake. Using a staging area, you can import the data into Snowflake after exporting it from MySQL to a CSV or SQL dump file.
  • Validation of Data and Quality Assurance:
    • Assure data integrity before and after migration by verifying data types, restrictions, and completeness.
    • Verify the correctness and consistency of the data after migration by running checks.
  • Enhance Information for Snowflake:
    • Take advantage of Snowflake’s performance optimizations.
    • Utilize clustering keys to arrange information.
    • Make use of Snowflake’s built-in automatic query optimization tools.
    • Think about using query pattern-based partitioning methods.
  • Manage Schema Changes and Data Transformations:
    • Adjust the MySQL schema to meet Snowflake’s needs. 
    • Snowflake supports semi-structured data, although the structure of the data may need to be changed.
    • Plan the necessary changes and carry them out during the migration process.
    • Verify that the syntax and functionality of SQL queries are compatible with Snowflake.

Troubleshooting Common Issues

  • Problems with Connectivity:
    • Verify that Snowflake and MySQL have the appropriate permissions and network setup.
    • Diagnose connectivity issues as soon as possible by utilizing monitoring and logging technologies.
  • Performance bottlenecks:
    • Track query performance both before and after the move.
    • Optimize SQL queries for the query optimizer and architecture of Snowflake.
  • Mismatches in Data Type and Format:
    • Identify and resolve format and data type differences between Snowflake and MySQL.
    • When migrating data, make use of the proper data conversion techniques.


You can now seamlessly connect MySQL to Snowflake using manual or automated methods. The manual method will work if you seek a more granular approach to your migration. However, if you are looking for an automated and zero solution for your migration, book a demo with Hevo.

Frequently Asked Questions

  1. Why should I migrate data from MySQL to Snowflake?
  • Performance and Scalability: Snowflake’s cloud-native architecture provides excellent performance and nearly limitless scalability.
  • Data Integration and Sharing: Snowflake makes secure data sharing inside departments and with outside partners easy.
  • Decreased Maintenance: Because Snowflake is a fully managed service, it frees up enterprises to concentrate on data analysis by taking care of database management duties.
  1. What are the critical steps involved in migrating data from MySQL to Snowflake?
  • Assessment and Planning
  • Data Extraction
  • Schema Migration
  • Data Loading
  • Data Validation
  • Optimization
  1. How can I ensure data integrity during migration?
  • Data Validation: Conduct pre and post-migration checks to compare data in MySQL and Snowflake. 
  • Schema Consistency: Ensure that the schema definitions in Snowflake match those in MySQL, with appropriate data type conversions and constraints.
  • Transaction Management: Handle transactions carefully during extraction and loading to avoid data inconsistencies.
  1. What are the challenges of real-time ETL for MySQL to Snowflake?
  • Data Latency
  • Consistency
  • Performance Impact
  • Error Handling
Nitin Birajdar
Lead Customer Experience Engineer

Nitin, a seasoned professional with 9 years of experience, excels in ETL, Data Engineering, SAAS, and AI as a Customer Experience Lead. His expertise drives innovation and excellence, ensuring optimal solutions for diverse data challenges.

All your customer data in one place.