MongoDB is a NoSQL database that stores objects in a JSON-like structure. It is usually classified as document-oriented storage because of the way it treats objects as documents. Schemaless databases like MongoDB offer unique versatility because of their ability to store semi-structured data.

MySQL, on the other hand, is a structured database with a hard schema. It is a usual practice to use NoSQL databases for use cases where the number of fields will evolve as the development progresses. At some point in time, when the use case matures itself, organizations will notice the overhead introduced because of their NoSQL schema and will want to migrate the data to hard-structured databases with the comprehensive querying ability and predictable query performance. 

This article will give you a brief overview of MongoDB and MySQL. You will learn how you can easily set up MongoDB to MySQL Integration using two methods. You will also explore the limitations of manually setting up MongoDB to MySQL Integration. In the end, you will be able to choose the best of both methods based on your business requirements. Read along to decide which method of connecting MongoDB to MySQL is best for you.


You will have a much easier time understanding the ways to set up the MongoDB to MySQL integration if you have gone through the following aspects:

  • An active MongoDB account.
  • An active MySQL account.
  • Working knowledge of Databases.
  • Working knowledge of Structured Query Language (SQL).

Methods to Set Up MongoDB to MySQL Integration

There are many ways of loading data from MongoDB to MySQL. In this article, you will be looking into two popular ways. In the end, you will have a good understanding of each of these two methods. This will help you to make the right decision based on your use case:

Method 1: Manual ETL Process to Set Up MongoDB to MySQL Integration

MongoDB and MySQL are extremely different databases with different schema strategies. This means there are a lot of things to consider before moving your data from a Mongo collection to MySQL. The simplest of the migration will contain the few steps below.

Use the default Mongoexport tool to create a CSV from the collection.

mongoexport --host localhost --db classdb --collection student --type=csv  --out students.csv --fields first_name,middle_name,last_name, class,email

In the above command, classdb is the database name, the student is the collection name and students.csv is the target CSV file that will contain data from MongoDB. An important point here is the –field attribute. This attribute should have all the lists of fields that you plan to export from the collection. If you think about it, MongoDB follows a schema-less strategy and there is no way to ensure that all the fields are there in all the documents.

If MongoDB was being used for the purpose it was designed for, there is a big chance that not all documents in the same collection have all the attributes. Hence while doing this export, you should be sure that these fields are there in all the documents. If the fields are not in all documents, MongoDB will not throw an error, but will just populate an empty value in its place.

Create a student table in MySQL to accept the new data.


Load the data into the MySQL table using the below command.

LOAD DATA LOCAL INFILE 'students.csv' INTO TABLE students FIELDS TERMINATED BY ','  ENCLOSED BY '"' LINES TERMINATED BY 'n' (firstname,middlename,lastname,class,email)

You have the data from MongoDB loaded into MySQL now.

Another alternative to this process would be to exploit the document storage capability of MySQL. This allows MongoDB documents to be directly loaded as a MySQL collection rather than a MySQL table. The caveat is that you will not be able to use the true power of MySQL’s structured data storage. And in most cases, that will be the reason why you chose to move the data to MySQL in the first place. 

But the above set of steps only works for a limited set of use cases and does not reflect the true challenges in migrating a collection from MongoDB to MySQL. Let us look into them in the next section.

Limitations of Manually Setting Up MongoDB to MySQL Integration

Though the manual method of setting up MongoDB to MySQL was effective, there were numerous limitations associated with this method. Below are some of the limitations associated with manually setting up MongoDB to MySQL Integration:

  1. In real life, there are going to be hundreds of fields in a collection, and creating the first command(the first step) itself is going to be a pain trying to manually write all the fields.
  2. This approach needs you to have access to an instance with enough storage space to store the whole contents of the collection and then execute the LOAD DATA command.
  3. In MongoDB use cases, typically there will be many attributes that do not exist in all the documents. This method will populate empty values in such cases. This is not a desirable behavior and in most cases, you will need to write a custom script to handle this.
  4. This method is suitable for a first-time bulk load, but if your use case needs frequent syncing, it would be tough to handle the duplicate data rows and scheduling. 
  5. This is going to be an extremely time-consuming process if the source collection is large. If MongoDB is being used for serving a live website or mobile app, the developer will need to build additional throttling logic so that the incoming traffic is not affected.
  6. You could also end up in trouble if the fields inside your MongoDB have arrays as their values. These will need to be specifically handled using a script since the simple CSV export can lead to unexpected results here. 

A solution to all these complexities will be to use a third-party cloud-based ETL tool like Hevo. Hevo can mask all the above concerns and provide an elegant migration process for your MongoDB collections. 

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Method 2: Using Hevo Data to Set Up MongoDB to MySQL Integration

MongoDB to MySQL: Hevo banner
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Hevo Data, a No-code Data Pipeline, helps you directly transfer data from MongoDB and 150+ other data sources to MySQL and other Databases, Data Warehouses, BI tools, or a destination of your choice in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

The steps to load data from MongoDB to MySQL using Hevo Data are as follows:

  • Connect your MongoDB account to Hevo’s platform. Hevo has an in-built MongoDB Integration that connects to your account within minutes.
Configuring MongoDB as Source in Hevo Data
Configuring MongoDB as Source in Hevo
  • Select MySQL as your destination to convert from MongoDB to MySQL and start moving your data.
Configuring MySQL as Destination in Hevo Data
Configuring MySQL as Destination in Hevo

Hevo automatically flattens all the nested JSON data coming from MongoDB and automatically maps it to MySQL destination without any manual effort. 

Here are more reasons to try Hevo to migrate from MongoDB to MySQL:

All of the capabilities, none of the firefighting  -:

Using manual scripts and custom code to move data into the warehouse is cumbersome. Frequent breakages, pipeline errors, and lack of data flow monitoring make scaling such a system a nightmare. Hevo’s reliable data pipeline platform enables you to set up zero-code and zero-maintenance data pipelines that just work.

Reliability at Scale – With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency. 

Monitoring and Observability – Monitor pipeline health with intuitive dashboards that reveal every stat of the pipeline and data flow. Bring real-time visibility into your ELT with Alerts and Activity Logs 

Stay in Total Control – When automation isn’t enough, Hevo offers flexibility – data ingestion modes, ingestion, and load frequency, JSON parsing, destination workbench, custom schema management, and much more – for you to have total control.    

Auto-Schema Management – Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with the destination warehouse so that you don’t face the pain of schema errors.

24×7 Customer Support – With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day full-featured free trial.

Transparent Pricing – Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spending. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow. 

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This article gives you a comprehensive guide to MongoDB and MySQL and how you can easily set up MongoDB to MySQL integration. It can be concluded that Hevo seamlessly integrates with MongoDB and MySQL ensuring that you see no delay in setup and implementation. Hevo will ensure that the data is available in your database in real-time. Hevo’s real-time streaming architecture ensures you have accurate, latest data in your database.

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Businesses can use automated platforms like Hevo Data to export MongoDB to MySQL 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 with a hassle-free experience.

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Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. Have a look at our unbeatable pricing, which will help you choose the right plan for you.

Share your experience of loading data from MongoDB to MySQL in the comment section below.

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