Data is the lifeblood of modern applications, and the storage that suited an application at prototype stage often stops fitting after a few years in production. This guide shows you how to move data from MongoDB to PostgreSQL, whether as a one-time migration or as an ongoing replication process.
You will get a short introduction to both databases, the key differences between them, and two ways to integrate them: a manual Extract, Transform, Load (ETL) process, and a no-code platform that automates the work and can run continuously with change data capture.
How Do You Move Data from MongoDB to PostgreSQL?
There are two main approaches. For a small, one-time move, you can export collections with mongoexport and load them into PostgreSQL with the COPY command. For production systems that need continuous, low-latency syncing, a no-code change data capture (CDC) tool such as Estuary captures inserts, updates, and deletes from MongoDB in real time and writes them to PostgreSQL, handling schema conversion automatically.
Choose your method:
- Real-time, no-code (Estuary): Set up a low-latency MongoDB to PostgreSQL pipeline in minutes. Best for production environments that need continuous syncing and built-in schema handling.
- Manual (CLI and SQL): Export with
mongoexportand load withCOPY. Best for small, one-time migrations, but not suited to growing systems or real-time needs.
What is MongoDB?
MongoDB is a document-oriented database that follows the NoSQL paradigm for storing, managing, and accessing data. Data is stored in JavaScript Object Notation (JSON)-like documents and does not need to follow a strict schema structure, as keys can be added dynamically.
MongoDB’s flexibility means it can store both structured and semi-structured data efficiently while handling high volumes of data with low latency. MongoDB is a cross-platform database, which means that it is available on various operating systems and cloud service providers.
For applications with changing data requirements, especially those where those requirements are not known ahead of time, it can be a good fit to reduce the friction associated with rapid iterations at the data model layer.
What is PostgreSQL?
PostgreSQL is an open-source Relational Database Management System (RDBMS) that is Structured Query Language (SQL) compliant and is known for its proven reliability, data integrity, and vibrant community. It also has a large feature set and supports extensibility as one of its core tenets.
PostgreSQL is a popular choice amongst developers whenever the requirement of the application to be designed has inherent relationships or interactions that have to be modeled to enable the efficient retrieval of records for analytical purposes.
PostgreSQL is Atomicity, Consistency, Isolation, and Durability (ACID) compliant. What that means is that transactions are either processed in full or not at all. PostgreSQL, therefore, avoids partial execution of operations, and data recovery in the case of failure can be more holistic and methodical.
Critical Differences Between MongoDB and PostgreSQL
The two databases take different approaches to structure, storage, and querying:
- MongoDB is document-oriented, while PostgreSQL is object-relational.
- MongoDB stores data as JSON-like BSON (Binary JSON) documents in collections; PostgreSQL stores data in tables. A MongoDB document is roughly equivalent to a PostgreSQL row.
- MongoDB does not require a strict schema, and documents in the same collection can have different keys. PostgreSQL enforces a schema, with columns defined at table creation.
- MongoDB uses the MongoDB Query Language, while PostgreSQL uses SQL.
- MongoDB combines and aggregates records with embedded documents and operators like
$lookup,$graphLookup, and$unionWith, while PostgreSQL uses JOINs and UNIONs.
When to Migrate vs. Replicate MongoDB to PostgreSQL
Data moves from MongoDB to PostgreSQL in two scenarios. A one-time migration copies the current state into PostgreSQL, for example when an application moves its system of record to a relational database. Ongoing replication keeps MongoDB as the source and PostgreSQL as a continuously updated destination, which is where CDC applies.
Common production uses for the replication case:
- Offloading analytical and reporting queries off MongoDB onto PostgreSQL, so heavy aggregations do not compete with the application's operational writes.
- Consolidating fragmented document collections (orders, users, products) into a relational model that supports joins and BI tools.
- Feeding a SQL-based dashboard or downstream service that expects relational tables.
The data typically moved is operational application data: orders, events, user records, and similar collections whose changes need to reach analytics quickly.
Method 1: Manual ETL with mongoexport and COPY
Extract from the source, shape it for the destination, then load it. For MongoDB to PostgreSQL that means exporting with mongoexport to CSV, creating a matching table, and importing with COPY.
Step 1: Export from MongoDB. Assumes an employeedb database with an employees collection:
bashmongoexport --host localhost --db employeedb --collection employees --type=csv \\
--out employees.csv --fields name,position,country,specialization
Step 2: Create a matching PostgreSQL table. The schema must mirror the CSV structure:
sqlCREATE TABLE employees (
id SERIAL PRIMARY KEY,
name VARCHAR NOT NULL,
position VARCHAR NOT NULL,
country VARCHAR NOT NULL,
specialization VARCHAR NOT NULL
);
Step 3: Load with COPY. Use the correct path for your OS (a Windows path is shown):
sqlCOPY employees(name, position, country, specialization)
FROM 'C:\\employees.csv' DELIMITER ',' CSV HEADER;
Where this breaks down: The manual method is fine for one small, flat collection. It falls apart the moment your documents contain nested objects or arrays, because mongoexport to CSV flattens or stringifies them and you lose structure. It also captures a single point in time, so PostgreSQL drifts out of date the instant MongoDB changes again. For more than a one-off, or for any collection with nested fields, use a CDC tool.
Method 2: No Code Data Integration Platforms
No code data integration platforms provide the means to migrate, replicate, or capture data from one database system to another without writing code. They typically automate the schema conversion process so data types are compatible across the databases that they support. They also provide a User Interface (UI) for easy interaction with the databases.
In this section, you will look at how to perform data integration between MongoDB and PostgreSQL using Estuary and Airbyte. To follow along, it is assumed that you have accounts with these service providers, as they all provide a cloud solution.
Estuary
The problem with keeping PostgreSQL in sync with MongoDB is not the initial copy. It is staying current without hammering the source, capturing deletes, and not losing your place when something goes offline. That is what a CDC platform is for.
Estuary is a real-time data integration platform that captures from MongoDB through change streams and writes to PostgreSQL, with SQL and TypeScript transformations in between. As a disclosure, Estuary is our own platform, so weigh this section accordingly.
- Who it is for: Teams that need PostgreSQL continuously updated from MongoDB for analytics or reporting, without building and operating their own streaming infrastructure.
- Why it works for this pair: Estuary performs an initial backfill, then reads change streams indefinitely to capture inserts, updates, and deletes. It is available as managed cloud, with Private Deployment and BYOC options for stricter security or data-residency needs.
- When not to use it: If you only need a single, one-time export of one flat collection, the manual method above is simpler than setting up a pipeline.
- Verify before committing: Confirm your MongoDB deployment is a replica set (Atlas clusters are by default), and test how deletes appear in PostgreSQL for your collections, since that depends on capture mode (see challenges below).
Estuary reports sub-100ms end-to-end latency. That figure describes the streaming path; real latency to PostgreSQL also depends on your network, the materialization's update cadence, and destination write speed, so treat it as the capture-side ceiling rather than a guarantee for every pipeline. See the MongoDB connector docs and PostgreSQL materialization docs for connector specifics.
The steps to achieve this are given below.
- Sign up for Estuary or log in if you already have an account.
- Make sure your existing MongoDB and PostgreSQL databases are ready to use with Estuary. See the prerequisites for MongoDB and PostgreSQL.
- Next, you will need to create a data pipeline that connects a source to a destination. Data pipelines are called “Data Flow” in Estuary speak. Sources are called “Captures” while destinations are called “Materializations.”
- Click on Captures on the left pane menu, then click on the New capture button.
- Next, select MongoDB as the Connector for your source.
- You will have to provide a Name for the capture and provide the necessary details, then click on Next.
- You can select collections or modify properties as you see fit. The collections are the datasets stored in Estuary; data captured from a source connection is stored in the cloud.
- Click on Next again if you made changes to your collections, and then click Save and publish.
- The next step is to click on Materialize collections.
- You will now select the Connector tile for the destination, which in this case will be PostgreSQL.
- Choose a unique Name for the materialization, then configure the connection details, then click Next. You can still choose to add or remove collections at this point.
- Finally, click on Save and publish.
Skip the manual steps and set up a real-time, no-code data pipeline with Estuary. Start your free trial or contact us to learn more about seamless data integration.
Airbyte
Airbyte is an open-source data movement platform that can be used to set up and manage data pipelines. It has integrations with various data sources, applications, and databases. Airbyte has a self-hosted and managed service. For this demonstration, you can use the managed service, and it will be focused on setting up the source and destination connectors and how to link them.
You can follow the steps below to capture data from MongoDB to PostgreSQL.
- First, log in to your Airbyte account.
- Next, you will need to set up a source connector. From your Airbyte dashboard, click on New source, then select MongoDB from the list of sources. You will need to provide the necessary connection details to a MongoDB database instance, then click on Set up source.
- Next, you will need to configure a destination connector. To do so click on Destinations in your Airbyte dashboard, then select Create destination and choose PostgreSQL.
- Enter the relevant connection parameters, then click Set up destination to complete the process.
- The last step is to set up the Airbyte connection between the source and the destination. To do so click on Connections in your Airbyte dashboard and choose New connection.
- Select the source and destination you created in an earlier step. The collections in MongoDB will be automatically detected and you can choose the replication mode and the frequency of replication.
- Click on the Set up connection button to start the syncing process.
Airbyte suits scheduled batch replication. It is not built for low-latency, real-time use, so if PostgreSQL needs to reflect MongoDB changes within seconds, a streaming platform is the better fit.
⚡ Looking for a truly real-time, low-latency solution? Estuary supports change data capture (CDC) from MongoDB to PostgreSQL with built-in schema conversion and a no-code UI.
👉 Start your free Estuary pipeline now — no credit card required.
Conclusion
In this article, you learned how to capture data from MongoDB to PostgreSQL via two methods: a manual ETL process and no-code data platforms. Before that, you were introduced to MongoDB and PostgreSQL database systems, and some of the major differences between them were outlined.
At this stage, you should have a robust understanding of migrating data from MongoDB to PostgreSQL and how data integration platforms like Estuary can help you simplify the process.
Estuary is free to try today. Have questions about this or any other Data Flow? Join the Estuary team in the community discussion, or check out the docs.
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