How to Move Data from MongoDB to PostgreSQL: 2 Easy Steps & Methods

Moving from MongoDB to PostgreSQL can be challenging, especially when dealing with unstructured or rapidly changing data. The differences in data formats, schemas, and query logic often complicate the process.

However, PostgreSQL offers strong advantages once the migration is complete. Its structured data model, advanced SQL support, and JSONB capabilities give you both stability and flexibility.

In this guide, we’ll explore two reliable ways to move your data. Whether you prefer manual control or a tool like Hevo to automate the process, there’s a method that fits your needs.

Methods to move data from MongoDB to PostgreSQL

Method 1: Using Hevo (No-Code Tool)

Hevo lets you transfer data from MongoDB to PostgreSQL automatically, no coding, no commands. Just follow these simple steps.

Step 1: Add MongoDB as Your Source

1. Log in to your Hevo Dashboard.
2. Click on “+ Create Pipeline” at the top right.
3. Under “Choose Source Type”, select MongoDB.
4. Enter your MongoDB connection details – this includes the hostname, port, database name, username, and password.
5. Test the connection, then click “Continue”.

Step 2: Add PostgreSQL as Your Destination

1. Now go to the Destination section in the pipeline setup.
2. Choose PostgreSQL as your destination type.
3. Fill in your PostgreSQL connection details – host, port, database name, username, and password.
4. Click “Test Connection” to make sure it works, then hit “Save Destination”.

Step 3: Configure Your Pipeline

1. Once source and destination are set, Hevo will guide you to the Data Mapping screen.
2. Here, you can let Hevo auto-map the fields from MongoDB to PostgreSQL, or manually adjust the schema as needed.
3. Use the Data Transformations option if you want to clean or reshape your data before loading.

Step 4: Activate and Run

1. Review the pipeline summary and click “Activate Pipeline”.
2. Hevo will now begin transferring data from MongoDB to PostgreSQL in real time.
3. You can monitor progress from the Pipeline Dashboard, and even set up alerts or logs to track updates.

Method 2: Manual Migration (Do It Yourself)

This method is great for small projects or for those who want full control. It follows the ETL method, which stands for Extract, Transform, and Load.

What’s Needed Before Starting

• MongoDB is installed (Version 4.4 works well).
• PostgreSQL installed (Version 12 or higher).
• A terminal or command line tool to type commands.
• A place on the computer to save a file in CSV format.

Step 1: Export Data from MongoDB

MongoDB has a tool called mongoexport. This tool pulls data from the database and saves it into a CSV file.

Here’s an example command to run in the terminal:

mongoexport --host localhost --db bookdb --collection books --type=csv --out books.csv --fields name,author,country,genre

This command creates a file called books.csv with the listed fields from the books collection in the bookdb database.

Step 2: Create a Table in PostgreSQL

A table needs to be made in PostgreSQL that matches the data from MongoDB. This way, the data knows where to go.

Here’s the SQL command to create the table:

CREATE TABLE books (  id SERIAL PRIMARY KEY,  name VARCHAR NOT NULL,  author VARCHAR NOT NULL,  country VARCHAR NOT NULL,  genre VARCHAR NOT NULL);

Each column matches the fields in the CSV file from MongoDB.

Step 3: Import the Data into PostgreSQL

Now it’s time to move the data into PostgreSQL using the COPY command.

Here’s what the command looks like:

COPY books(name,author,country,genre)FROM ‘C:/path/to/books.csv’ DELIMITER ‘,’ CSV HEADER;

This command tells PostgreSQL to load data from the CSV file into the books table.

Manual Method: Pros and Cons

Pros:

• Works well for small projects with limited data.
• No need for extra tools, just basic scripts or commands.

Cons:

• It can be confusing if you’re not familiar with databases or scripting.
• Easy to make mistakes like typos or missing schema details.
• Gets slow with large data volumes.

What is MongoDB?

MongoDB is a modern, document-oriented NoSQL database designed to handle large amounts of rapidly changing, semi-structured data. Unlike traditional relational databases that store data in rigid tables, MongoDB uses flexible JSON-like documents with dynamic schemas, making it an ideal choice for agile development teams building highly scalable and available internet applications.

At its core, MongoDB features a distributed, horizontally scalable architecture that allows it to scale out across multiple servers as data volumes grow easily. Data is stored in flexible, self-describing documents instead of rigid tables, enabling faster iteration of application code.

You can also take a look at MongoDB’s key use cases to get a better understanding of how it works.

What is PostgreSQL?

PostgreSQL is a powerful, open-source object-relational database system that has been actively developed for over 35 years. It combines SQL capabilities with advanced features to store and scale complex data workloads safely.

One of PostgreSQL’s core strengths is its proven architecture focused on reliability, data integrity, and robust functionality. It runs on all major operating systems, has been ACID-compliant since 2001, and offers powerful PostgreSQL extensions like the popular PostGIS for geospatial data.

What are the Benefits of Migrating MongoDB Data to PostgreSQL?

• Enhanced Security: Synchronization tools often come with built-in encryption and access controls, ensuring secure data transfer between databases.
• Enhanced Data Consistency: Synchronizing data ensures consistency across MongoDB and PostgreSQL, eliminating discrepancies that could affect analytics, reporting, or application performance.
• Improved Data Availability: Real-time replication provides up-to-date data in both databases, ensuring uninterrupted access to critical information for applications and users.
• Real-Time Decision-Making: By maintaining synchronized data, organizations can enable real-time analytics and faster decision-making, which is critical for dynamic business environments.
• Scalability: Synchronization supports distributed systems, allowing businesses to scale operations effectively by leveraging the strengths of both databases, MongoDB’s document-based architecture and PostgreSQL’s relational capabilities.
• Operational Efficiency: With synchronized databases, teams can optimize workflows, streamline ETL processes, and reduce redundancy in data management.

Also, you can check out how you can migrate data from MongoDB to MySQL easily and explore other destinations where you can sync your MongoDB data.

Differences between MongoDB & PostgreSQL

MongoDB is a distributed database that excels in handling modern transactional and analytical applications, particularly for rapidly changing and multi-structured data. On the other hand, PostgreSQL is an SQL database that provides all the features I need from a relational database.

The differences between MongoDB & PostgreSQL include the following:

• Data Model: MongoDB uses a document-oriented data model, but PostgreSQL uses a table-based relational model.
• Query Language: MongoDB uses query syntax, but PostgreSQL uses SQL.
• Scaling: MongoDB scales horizontally through sharding, but PostgreSQL scales vertically on powerful hardware.
• Community Support: PostgreSQL has a large, mature community support, but MongoDB’s is still growing.

If you want a deeper comparison, check out this blog: MongoDB vs PostgreSQL

Performance Monitoring and Verification Post-Migration

Ensuring a smooth transition during data migration involves not only moving data but also maintaining its accuracy, performance, and reliability in the new environment. Here are key aspects to focus on:

1. Real-Time Monitoring and Error Handling

• Why It Matters: Real-time monitoring helps identify bottlenecks and errors during the migration process, ensuring issues are addressed promptly.
• Best Practices: Utilize tools that provide detailed logs and alert mechanisms for migration tasks, allowing for quick error resolution.

2. Verifying Data Integrity

• Why It Matters: Ensuring that the migrated data matches the source data is crucial for maintaining trust in the system and supporting decision-making.
• Action Steps:
  • Perform row-level and column-level data comparisons.
  • Use checksum or hash functions to validate data consistency.
  • Conduct spot checks for high-priority data.

3. Post-Migration Performance Adjustments

Why It Matters: Migrated systems often require tuning to achieve optimal performance in the new environment.

Action Steps:

• Monitor query execution times and resource usage.
• Adjust indexing, caching, and partitioning strategies.
• Leverage performance monitoring tools to identify and resolve inefficiencies.

Best Practices For a Seamless Migration

• Data Compatibility Assessment: Differences between MongoDB’s schema-less JSON-compatible storage and PostgreSQL’s relational schema should be reviewed in order to conduct transformations appropriately.
• Schema Definition: The PostgreSQL schema should be defined in an optimized way that realizes relational constraints, indexes, normalization, data integrity, and performance enhancement.
• Use ETL Tools: Leverage ETL tools like Hevo Data, Talend, or custom scripts to carry out suitable data transformation and migration.
• Batch Data Migration: Migrate large amounts of data in batches for better performance and error recovery.
• Indexing and Performance Tuning: Optimize indexes, queries, and partitioning in PostgreSQL for better performance post-migration.
• Data Validation: Compare record counts, data types, and integrity between MongoDB and PostgreSQL to validate the accomplished migration.
• Handle Nested Data: MongoDB’s nested documents should be flattened or restructured into relational tables, or PostgreSQL’s JSON/JSONB can be used to carry semi-structured data. 

What’s your pick?

When deciding how to migrate your data from MongoDB to PostgreSQL, the choice largely depends on your specific needs, technical expertise, and project scale.

• Manual Method: If you prefer granular control over the migration process and are dealing with smaller datasets, the manual ETL approach is a solid choice. This method allows you to manage every step of the migration, ensuring that each aspect is tailored to your requirements.
• Hevo Data: If simplicity and efficiency are your top priorities, Hevo Data’s no-code platform is perfect. With its seamless integration, automated schema mapping, and real-time transformation features, Hevo Data offers a hassle-free migration experience, saving you time and reducing the risk of errors.

Conclusion

Migrating data from MongoDB to PostgreSQL can be seamless with the right approach. Whether you opt for the manual ETL method for greater control or Hevo’s automated no-code platform for efficiency, planning, schema mapping, and performance optimization is key. By ensuring data integrity, monitoring post-migration performance, and following best practices, you can unlock PostgreSQL’s full potential.

Want to simplify your data migration? Sign up for a free trial of Hevo today and experience effortless, automated data transfers!

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