Is your MySQL server getting too slow for analytical queries now? Or are you looking to join data from another Database while running queries? Whichever your use case, it is a great decision to move the data from MySQL to Redshift for analytics.

This post covers the detailed steps you need to follow to migrate data from MySQL to Redshift. You will also get a brief overview of MySQL and Amazon Redshift. You will also explore the challenges involved in connecting MySQL to Redshift using custom ETL scripts. Let’s get started.

Methods to Set up MySQL to Redshift

There are different methods to set up MySQL to Redshift:

Ways to Set up MySQL to Redshift Integration

Method 1: Manually Set up MySQL to Redshift Integration

MySQL provides a COPY command that allows you to extract data programmatically in SQL Files. This data needs to be converted into CSV format because SQL format is not supported by Redshift. Next, you would need to prepare this data and load it to Amazon S3 and then to Redshift. This would need you to invest in deploying dev resources, who understand both MySQL and Redshift infrastructures, and can set up the data migration from scratch. This would also be a time-consuming approach.

Method 2: Using Hevo Data to Set up MySQL to Redshift Integration

Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs.

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Method 1: Using Hevo to Set up MySQL to Redshift Integration

Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. With integration with 150+ Data Sources (40+ free sources), we help you not only export data from sources & load data to the destinations but also transform & enrich your data, & make it analysis-ready.

The following steps can be implemented to set up MySQL to Redshift Migration using Hevo:

  • Configure Source: Connect Hevo Data with Oracle by providing a unique name for your Pipeline along with information about your MySQL database such as its name, IP Address, Port Number, Username, Password, etc.
Configuring the MySQL Source - MySQL to Redshift
Source: Self
  • Integrate Data: Complete MySQL to Redshift Migration by providing your MySQL database and Redshift credentials such as your authorized Username and Password, along with information about your Host IP Address and Port Number value. You will also need to provide a name for your database and a unique name for this destination.
Configuring the Redshift Destination - MySQL to Redshift
Source: Self
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Advantages of Using Hevo

There are a couple of reasons why you should opt for Hevo over building your own solution to migrate data from CleverTap to Redshift.

  • Automatic Schema Detection and Mapping: Hevo scans the schema of incoming CleverTap automatically. In case of any change, Hevo seamlessly incorporates the change in Redshift. 
  • Ability to Transform Data – Hevo allows you to transfer data both before and after moving it to the Data Warehouse. This ensures that you always have analysis-ready data in your Redshift Data Warehouse.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.

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Method 2: Incremental Load for MySQL to Redshift Integration

Now, since you have the idea about MySQL and Amazon Redshift, it’s time to move on to the method to connect MySQL to Redshift using custom ETL scripts. You can follow the below-mentioned steps to connect MySQL to Redshift.

Step 1. Dump the Data into Files

The most efficient way of loading data in Amazon Redshift is through the COPY command that loads CSV/JSON files into the Amazon Redshift. So, the first step is to bring the data in your MySQL database to CSV/JSON files.

There are essentially two ways of achieving this:

1) Using mysqldump command.
mysqldump -h mysql_host -u user database_name table_name --result-file table_name_data.sql

The above command will dump data from a table table_name to the file table_name_data.sql. But, the file will not be in CSV/JSON format required for loading into Amazon Redshift. This is how a typical row may look like in the output file:

INSERT INTO `users` (`id`, `first_name`, `last_name`, `gender`) VALUES (3562, ‘Kelly’, ‘Johnson’, 'F'),(3563,’Tommy’,’King’, 'M');

The above rows will need to be converted to the following format:

"3562","Kelly","Johnson", "F"
2) Query the data into a file.
mysql -B -u user database_name -h mysql_host  
-e "SELECT * FROM table_name;" |  
sed "s/'/'/;s/t/","/g;s/^/"/;s/$/"/;s/n//g"  
> table_name_data.csv

You will have to do this for all tables:

for tb in $(mysql -u user -ppassword database_name -sN -e "SHOW TABLES;"); do
     echo .....;

Step 2. Clean and Transform

There might be several transformations required before you load this data into Amazon Redshift. e.g. ‘0000-00-00’ is a valid DATE value in MySQL but in Redshift, it is not. Redshift accepts ‘0001-01-01’ though. Apart from this, you may want to clean up some data according to your business logic, you may want to make time zone adjustments, concatenate two fields, or split a field into two. All these operations will have to be done over files and will be error-prone.

Step 3. Upload to S3 and Import into Amazon Redshift

Once you have the files to be imported ready, you will upload them to an S3 bucket. Then run copy command:

COPY table_name FROM 's3://my_redshift_bucket/some-path/table_name/' credentials  

Again, the above operation has to be done for every table.

Once the COPY has been run, you can check the stl_load_errors table for any copy failures. After completing the aforementioned steps, you can migrate MySQL to Redshift successfully.

In a happy scenario, the above steps should just work fine. However, in real-life scenarios, you may encounter errors in each of these steps. e.g. :

  • Network failures or timeouts during dumping MySQL data into files.
  • Errors encountered during transforming data due to an unexpected entry or a new column that has been added
  • Network failures during S3 Upload.
  • Timeout or data compatibility issues during Redshift COPY. COPY might fail due to various reasons, a lot of them will have to be manually looked into and retried.

Challenges of Connecting MySQL to Redshift using Custom ETL Scripts

Challenges in Connecting MySQL to Redshift
Image Source

The custom ETL method to connect MySQL to Redshift is effective. However, there are certain challenges associated with it. Below are some of the challenges that you might face while connecting MySQL to Redshift:

  1. In cases where data needs to be moved once or in batches only, the custom script method works. This approach fails if you have to move data from MySQL to Redshift in real-time.
  2. Incremental load (change data capture) becomes tedious as there will be additional steps that you need to follow to achieve the connection.
  3. Often, when you write code to extract a subset of data, those scripts break as the source schema keeps changing or evolving. This can result in data loss.

The process mentioned above is brittle, error-prone, and often frustrating. These challenges impact the consistency and accuracy of the data available in your Amazon Redshift in near real-time. These were the common challenges that most users find while connecting MySQL to Redshift.

Method 3: Change Data Capture With Binlog

The process of applying changes made to data in MySQL to the destination Redshift table is called Change Data Capture (CDC).

You need to use the Binary Change Log (binlog) in order to apply the CDC technique to a MySQL database. Replication may occur almost instantly when change data is captured as a stream using Binlog.

Binlog records table structure modifications like ADD/DROP COLUMN in addition to data changes like INSERT, UPDATE, and DELETE. Additionally, it guarantees that Redshift also deletes records that are removed from MySQL. 

Getting Started with Binlog

When you use CDC with Binlog, you are actually writing an application that reads, transforms, and imports streaming data from MySQL to Redshift.

You may accomplish this by using an open-source module called mysql-replication-listener. A streaming API for real-time data reading from MySQL bBnlog is provided by this C++ library. For a few languages, such as python-mysql-replication (Python) and kodama (Ruby), a high-level API is also offered. 

Drawbacks using Binlog

Building your CDC application requires serious development effort.

Apart from the above-mentioned data streaming flow, you will need to construct:

Transaction management: In the event that a mistake causes your program to terminate while reading Binlog data, monitor data streaming performance. You may continue where you left off, thanks to transaction management.

Data buffering and retry: Redshift may also stop working when your application is providing data. Unsent data must be buffered by your application until the Redshift cluster is back up. Erroneous execution of this step may result in duplicate or lost data. 

Table schema change support: A modification to the table schema The ALTER/ADD/DROP TABLE Binlog event is a native MySQL SQL statement that isn’t performed natively on Redshift. You will need to convert MySQL statements to the appropriate Amazon Redshift statements in order to enable table schema updates.

Method 4: Using custom ETL scripts 

Step 1: Configuring a Redshift cluster on Amazon

Make that a Redshift cluster has been built, and write down the database name, login, password, and cluster endpoint.

Step 2: Creating a custom ETL script

Select a familiar and comfortable programming language (Python, Java, etc.).

Install any required libraries or packages so that your language can communicate with Redshift and MySQL Server.

Step 3: MySQL data extraction

  • Connect to the MySQL database.
  • Write a SQL query to extract the data you need. You can use this query in your script to pull the data.

Step 4: Data transformation

You can perform various data transformations using Python’s data manipulation libraries like `pandas`.

Step 5: Redshift data loading

With the received connection information, establish a connection to Redshift.

Run the required instructions in order to load the data. This might entail establishing schemas, putting data into tables, and generating them.

Step 6: Error handling, scheduling, testing, deployment, and monitoring

Try-catch blocks should be used to handle errors. Moreover, messages can be recorded to a file or logging service.

To execute your script at predetermined intervals, use a scheduling application such as Task Scheduler (Windows) or `cron` (Unix-based systems).

Make sure your script handles every circumstance appropriately by thoroughly testing it with a variety of scenarios.

Install your script on the relevant environment or server.

Set up your ETL process to be monitored. Alerts for both successful and unsuccessful completions may fall under this category. Examine your script frequently and make any necessary updates. 

Don’t forget to change placeholders with your real values (such as `}, `}, `}, etc.). In addition, think about enhancing the logging, error handling, and optimizations in accordance with your unique needs.

Disadvantages of using ETL scripts for MySQL Redshift Integration

  • Lack of GUI: The flow could be harder to understand and debug.
  • Dependencies and environments: Without modification, custom scripts might not run correctly on every operating system.
  • Timelines: Creating a custom script could take longer than constructing ETL processes using a visual tool. 
  • Complexity and maintenance: Writing bespoke scripts takes more effort in creation, testing, and maintenance.
  • Restricted Scalability: Performance issues might arise from their inability to handle complex transformations or enormous volumes of data.
  • Security issues: Managing sensitive data and login credentials in scripts needs close oversight to guarantee security.
  • Error Handling and Recovery: It might be difficult to develop efficient mistake management and recovery procedures. In order to ensure the reliability of the ETL process, it is essential to handle various errors.

Why Replicate Data From MySQL to Redshift?

There are several reasons why you should replicate MySQL data to the Redshift data warehouse.

Maintain application performance.

Analytical queries can have a negative influence on the performance of your production MySQL database, as we have already discussed. It could even crash as a result of it. Analytical inquiries need specialized computer power and are quite resource-intensive.

Analyze ALL of your data.

MySQL is intended for transactional data, such as financial and customer information, as it is an OLTP (Online Transaction Processing) database. But, you should use all of your data, even the non-transactional kind, to get insights. Redshift allows you to collect and examine all of your data in one location.

Faster analytics.

Because Redshift is a data warehouse with massively parallel processing (MPP), it can process enormous amounts of data much faster. However, MySQL finds it difficult to grow to meet the processing demands of complex, contemporary analytical queries. Not even a MySQL replica database will be able to match Redshift’s performance.


Instead of the distributed cloud infrastructure of today, MySQL was intended to operate on a single-node instance. Therefore, time- and resource-intensive strategies like master-node setup or sharding are needed to scale beyond a single node. The database becomes even slower as a result of all of this.

Above mentioned are some of the use cases of MySQL to Redshift replication.

Before we wrap up, let’s cover some basics.

Why Do We Need to Move Data from MySQL to Redshift?

Every business needs to analyze its data to get deeper insights and make smarter business decisions. However, performing Data Analytics on huge volumes of historical data and real-time data is not achievable using traditional Databases such as MySQL. MySQL can’t provide high computation power that is a necessary requirement for quick Data Analysis. Companies need Analytical Data Warehouses to boost their productivity and run processes for every piece of data at a faster and efficient rate.

Amazon Redshift is a fully managed Could Data Warehouse that can provide vast computing power to maintain performance and quick retrieval of data and results. Moving data from MySQL to Redshift allow companies to run Data Analytics operations efficiently. Redshift columnar storage increases the query processing speed.


This article provided you with a detailed approach using which you can successfully connect MySQL to Redshift. You also got to know about the limitations of connecting MySQL to Redshift using the custom ETL method. Big organizations can employ this method to replicate the data and get better insights by visualizing the data. Thus, connecting MySQL to Redshift can significantly help organizations to make effective decisions and stay ahead of their competitors.

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Businesses can use automated platforms like Hevo Data to set this integration and handle the ETL process. It helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you a hassle-free experience.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at the unbeatable Hevo pricing that will help you choose the right plan for your business needs.

Share your experience of connecting MySQL to Redshift in the comments section below!

Founder and CTO, Hevo Data

Sourabh has more than a decade of experience building scalable real-time analytics and has worked for companies like Flipkart, tBits Global, and Unbxd. He is experienced in technologies like MySQL, Hibernate, Spring, CXF, php, ExtJS and Shell.

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