Organizations rely on multiple databases and cloud-based solutions for their ever-expanding data storage needs. However, for in-depth analysis of organizational data, it is essential to perform seamless integrations between different platforms. 

Centralizing data in a data warehouse like Snowflake can help you obtain valuable insights for enhancing decision-making. You can connect any database, including MySQL on Amazon RDS, with Snowflake to create a single source of truth for in-depth analysis of business operations. Connecting MySQL on Amazon RDS to Snowflake can also help you leverage the advanced analytics capabilities of Snowflake. It is a unified database with cross-cloud, storage-decoupling, and data cloning capabilities. 

This article discusses the two methods for connecting Amazon RDS on MySQL and Snowflake. Based on your requirements, you can determine which method is better suited.

How to Connect MySQL on Amazon RDS to Snowflake?


  • An active Amazon RDS MySQL instance.
  • An active Snowflake account.
  • Enable the required permissions for data migration to your Snowflake account.

Method 1: Using CSV Export/Import for Moving Data from MySQL on Amazon RDS to Snowflake

To load data from MySQL on Amazon RDS to Snowflake using CSV export/import, follow these steps:

Step 1: Export MySQL on Amazon RDS data into CSV File

  • Login to AWS Management Console and open the Amazon RDS service.
  • Use the MySQL command-line client for connecting to the Amazon RDS instance.
  • Execute the SELECT statement for retrieving data with the following syntax:
SELECT * FROM table_name

Replace table_name with your unique Amazon RDS MySQL table name.

  • Export the data into a CSV file by modifying your SELECT statement with the INTO OUTFILE clause:
SELECT * INTO OUTFILE S3 ‘s3://bucket_name/path/to/table_name.csv
FROM users;
  • Now, download your CSV file with MySQL on Amazon RDS data through the AWS CLI command:
aws s3 cp s3://bucket-name/path/to//users.csv /path/on/local/machine/users.csv

Step 2: Import the CSV File into Snowflake Database

  • Log in to your Snowflake account.
  • Create your Snowflake stage using the following syntax:
create or replace stage csv_file_name_stage;
  • Use the FILE FORMAT command to describe CSV as the format to be imported:
create or replace file format csv_file_name_format type - ‘csv’ field_delimiter = ‘ , ‘;
  • Upload your CSV file from the local folder to the Snowflake stage by using the PUT command:

For Windows:

put file: //C:\test\csv_file_name.csv @csv_file_name_stage;

For Mac/Linux:

put file:///tmp/data/csv_file_name.csv @_csv_file_name_stage;
  • Create a table within the Snowflake database with a similar structure as the CSV file you want to import. 
create or replace table csv_file_name (
id integer,
Name varchar (100),
Location varchar (100)
  • Load the data from your Snowflake stage to the Snowflake database table by using COPY INTO command:
copy into test.csv_file_name from @csv_file_name_stage;
  • Now cross-verify if the Snowflake database table is loaded with the data by using the following statement:
select * from csv_file_name;

The CSV export/import approach is beneficial in the following use cases:

  • Best for Occasional Migration: If you do not have to move data from Amazon RDS MySQL to Snowflake consistently, the CSV export/import method can be an ideal option. You won’t need extensive resources to build a robust data pipeline for a one-time or occasional data migration. 
  • No Third-Party Tool Dependency: The manual method utilizes standard CSV files and leverages the built-in import/export capabilities of MySQL and PostgreSQL. As a result, you won’t have to rely on external tools or additional software, making the migration process simpler. 

There are certain limitations to using the CSV export/import for MySQL on Amazon RDS to Snowflake ETL, such as:

  • Data Quality Issues: Exporting and importing MySQL on Amazon RDS data to Snowflake through CSV files might be prone to human errors. This might lead to duplicate or incorrect data transfer, resulting in inaccurate insights. This can result in obtaining inaccurate insights. 
  • Inability to Handle Large Datasets: Migrating Amazon RDS MySQL data through a CSV export/import is a resource-intensive and time-consuming task, especially while handling massive volumes of data.
  • Lacks Real-Time Integration: A continuous data transfer isn’t possible when you are moving data from Amazon RDS MySQL to Snowflake through CSV export/import method. The data keeps on changing over time, and you will need updated stats for a complete analysis of the business operations. The CSV export/import will demand frequent manual migrations to make that happen, which isn’t efficient. 

Method 2: Using a No-Code Tool for Moving Data from MySQL on Amazon RDS to Snowflake

Using a No-Code tool for transferring data from MySQL on Amazon RDS to Snowflake helps overcome the limitations of the previous method. Here are some benefits associated with no-code tools:

  • Real-Time Data Synchronization: No-code tools support real-time data synchronization through Change Data Capture (CDC). CDC can capture any change in data at Amazon RDS MySQL and update it to Databricks. It will assist you in obtaining updated data for analysis as soon as the data is regenerated at the source.
  • Cost Savings: Most no-code tools price based on the usage and data volume, helping reduce expenses. It also eliminates the organization’s dependency on expensive technology and specialists for managing critical business data.

Hevo Data, a popular no-code ETL tool, can help you connect MySQL on Amazon RDS to Snowflake. It enables you to extract, transform, and load data to your preferred destination, all in near real-time.

Here are the steps for MySQL on Amazon RDS to Snowflake integration with Hevo Data:

Step 1: Configure MySQL on Amazon RDS as the Source

MySQL on Amazon RDS to Snowflake: Configure Source

Step 2: Configure Snowflake as the Destination

MySQL on Amazon RDS to Snowflake: Configure Destination

It will only take a few minutes to complete these steps and automate the data migration process from MySQL on AmazonRDS to Snowflake. 

What Can You Achieve by Migrating Data from MySQL on Amazon RDS to Snowflake?

Here’s what you can expect to achieve by migrating data from Amazon RDS MySQL to Snowflake:

  • Centralize your data from websites and mobile applications to support better analysis. 
  • Track the response of customers towards specific marketing campaigns and improve future strategies.
  • Identify any possible pattern in the collected data, which can help uncover any event or discrepancy in business workflows.
  • Determine the most popular content that is driving sales.


Moving data from MySQL on AmazonRDS to Snowflake would enhance your data interoperability capabilities in a centralized warehouse. You can replicate data either using CSV files or through a no-code tool.

MySQL on Amazon RDS to Snowflake migration through CSV export/import method is cost-effective and best for one-time migrations. However, it is time-consuming and is also prone to human errors. To overcome the limitations of using CSV files, you can use a no-code tool like Hevo Data. It is one of the most efficient tools to implement no-code ETL, which helps you automate the process of data transfers.

With Hevo, connecting Amazon RDS MySQL to Snowflake takes only a few minutes. You don’t require technical expertise to set up and start using the platform. Additionally, Hevo complies with industry-standard data security certifications, including SOC II, HIPAA, and GDPR.

If you don’t want SaaS tools with unclear pricing that burn a hole in your pocket, opt for a tool that offers a simple, transparent pricing model. Hevo has 3 usage-based pricing plans starting with a free tier, where you can ingest up to 1 million records.

Schedule a demo to see if Hevo would be a good fit for you, today!

Tejaswini Kasture
Freelance Technical Content Writer, Hevo Data

Tejaswini's profound enthusiasm for data science and passion for writing drive her to create high-quality content on software architecture, and data integration.

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