MongoDB is the preferred choice for most use cases involving structured and semi-structured data. MongoDB has a comprehensive querying layer, combined with the ability to add keys dynamically. This makes it a suitable choice for evolving requirements. In some cases, as time passes, the use cases mature, leading to table structures getting frozen. And the flexibility of a NoSQL database starts losing importance.

At this stage, most companies will try to move data from the NoSQL database to a conventional relational database. This provides even better-querying abilities. This post will cover the methods to move data from MongoDB to PostgreSQL using the manual ETL process and via Hevo Data.

What is MongoDB?

MongoDB is a document-oriented database which uses NoSQL for storing, handling, and accessing data. MongoDB data is stored in JavaScript Object Notation (JSON)-like files. As you can add the keys dynamically, we do not need to follow a stringent schema structure.  

MongoDB allows you to store both structured and semi-structured data even when there is huge data volume and handling high volumes of data with low latency. As it’is a cross-platform database, you can get it on different OSs and cloud service providers. 

When the data needs of applications change, mongoDB will be a suitable choice to reduce the friction associated with fast iterations at the data model layer.

What is PostgreSQL?

PostgreSQL is a popular RDBMS that uses SQL and has advantages such as reliability, data integrity, and an active community. It supports extensibility through its features. It will be a good fit for you when the application you are designing has inherent relationships or interactions that you need to model to enable for fetching records efficiently for analytics.

This RDBMS is compliant with Atomicity, Consistency, Isolation, and Durability (ACID). Therefore, the transactions are either processed in full or not. And, this removes partial execution of operations, and this allows a defined data recovery in the case of failure.

Why migrate from MongoDB to PostgreSQL?

  • Data consistency is crucial to ensure data reliability and accuracy. When data is inconsistent and spread across multiple systems, it can lead to errors, inefficiencies, and misinformation. Integrating data from MongoDB to PostgreSQL helps to maintain data consistency and coherence across the entire data ecosystem.
  • When data is stored in isolated systems or departments, it leads to data silos and hinders the process of seamless data sharing and collaboration. Synchronizing data between MongoDB and PostgreSQL breaks down the silos and enables a unified view of the organizational data.
  • Integrating data between MongoDB and PostgreSQL is important to run business operations smoothly; it helps in real-time decision-making and also improves operational efficiency.

Methods to Set Up MongoDB to PostgreSQL Integration

This section talks about the first method to export mongodb to PostgreSQL in depth. Additionally, the blog also talks about the limitations of MongoDB ETL. Towards the end, the blog discusses a simpler alternative to move data from MongoDB to PostgreSQL. Below are the two methods of MongoDB to PostgreSQL migration:

Method 1: Using Hevo Data to Set Up MongoDB to PostgreSQL Integration

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

  • Step 1: 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 a source in Hevo.
Configure MongoDB Source

Read more about using MongoDB as a source connector with Hevo.

  • Step 2: Select PostgreSQL as your destination and start migrating from MongoDB to PostgreSQL.
Configuring PostgreSQL as a destination in Hevo.
Configure PostgreSQL Destination

Read more about using PostgreSQL as a destination connector at Hevo. This is how simple it is to convert MongoDB to PostgreSQL using Hevo.

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Method 2: Manual ETL Process to Set Up MongoDB to PostgreSQL Integration

Copying data from MongoDB to PostgreSQL is usually accomplished using the mongoexport command and COPY command in PostgreSQL. Here are the broad steps:

Let us try to understand this process to migrate data from MongoDB to PostgreSQL with the help of an example. 

Step 1: Extract Data from MongoDB using mongoexport Command

We will first use the mongoexport command to create a CSV file from an already existing Mongo collection.

mongoexport --host localhost --db productdb --collection products --type=csv 
--out products.csv --fields name,category,color, brand,description

Here the database name is productdb, the collection name is products and products.csv is the output file we expect. The last attribute fields will contain the names of the keys that will be exported to CSV. This is very important because MongoDB does not maintain a hard structure of keys and it is very much possible that not all keys are present in all the documents.

The developer will have to ensure the keys that need to be present in CSV are specified. In case there is no such key present in a document, mongoexport will not throw any error. It will just silently populate that column as a blank value. If not careful enough, this can lead to unexpected results.

Step 2: Create a Product Table in PostgreSQL to Add the Incoming Data

CREATE TABLE products (
id SERIAL PRIMARY KEY,
name VARCHAR NOT NULL,
category VARCHAR NOT NULL,
color VARCHAR  NOT NULL,
brand VARCHAR  NOT NULL,
description VARCHAR NOT NULL
)

Step 3: Load the Exported CSV to PostgreSQL

COPY products(name,category,color, brand,description)
FROM 'C:tmppersons.csv' DELIMITER ',' CSV HEADER;

And that completes the process. Even though these steps may seem simple, it is to be noted that this is an oversimplified version of the real data migration problem. MongoDB and PostgreSQL are very different databases and there are a number of factors that can lead to unforeseen errors in this migration. Let’s look at some of the cases of MongoDB to Postgresql replication which can get us into trouble while using the above approach.

This concludes the process to migrate MongoDB to Postgres.

Limitations of Manual ETL Process to Set Up MongoDB to PostgreSQL Integration

Though the manual ETL process of setting up MongoDB to PostgreSQL Integration is effective, however, it has certain limitations. Below are a few of the limitations associated with MongoDB to PostgreSQL Integration:

  1. In typical use cases, there will be a large number of keys in a document and thereby in a MongoDB collection. Since there is nothing that restricts developers from adding more keys on the fly, most MongoDB collections will have hundreds of keys. Finalizing and specifying the keys to export in itself will be a big challenge.  
  2. Since an intermediate file is involved and that file contains the whole of a MongoDB collection, this approach requires the developer to have access to an instance with enough storage space to temporarily store the file.
  3. Normally, not all keys in the MongoDB collection will be present in all the documents in that collection. This method does not explicitly do anything to handle the case of non-existent keys. This will have to be specifically handled in usual scenarios.
  4. Many a time, the copying data is not a one-time process, but a periodic process that keeps syncing the two databases. In that case, this will need to be modified using custom logic to accommodate the scheduling and handling of duplicate data.
  5. For large MongoDB collections that serve a production application, there is a risk of this process affecting the response time of the incoming traffic. In such cases, it is imperative that the developer implements some throttling logic.
  6. Another source of trouble could be the presence of arrays as field values in your MongoDB collection. A simple CSV export could lead to unexpected results here.

Critical Differences Between MongoDB and PostgreSQL

Here are some notable distinctions between MongoDB and PostgreSQL listed below:

  • MongoDB is a document-oriented database, while PostgreSQL is an object-relational database.
  • MongoDB stores data in collections of JSON-like documents called Binary JSON (BSON), whereas data in PostgreSQL is stored in tables.
  • MongoDB documents in a collection are the equivalent of rows in PostgreSQL.
  • In MongoDB, you don’t have to follow a strict schema definition as not all documents in a collection may have the same keys, whereas, in PostgreSQL, the schema definition is strict as columns in a table are specified at creation.
  • MongoDB uses the MongoDB Query Language for interacting with the database whereas PostgreSQL utilizes SQL.

MongoDB uses embedded documents, $lookup$graphLookup, and $unionWith operations to combine records and perform aggregation, whereas PostgreSQL uses JOINs and UNIONs.

Use Cases of MongoDB to PostgreSQL Migration

Here are some practical applications of MongoDB to PostgreSQL Migration:

  • Data Analytics: Migrating from MongoDB to PostgreSQL empowers you to manage your data effectively, as you can perform advanced querying to ensure data integrity and scalability.
  • Gaming Industry: PostgreSQL can handle high traffic, which makes it suitable for the gaming industry 
  • Finance Industry: It is ideal for OLTP (Online Transaction Processing) workloads as it fully supports ACID, making it useful in the financial industry.
  • Geographical Information System: It is used in location based services and GIS because of built-in support for geographic data.
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Conclusion

This article provided an introduction to MongoDB and PostgreSQL and explained their features.

The main inferences from the article are:

  1. It elaborated on the 2 methods which you can use to set up MongoDB to PostgreSQL integration.
  2. The manual method on how to move data from MongoDB to PostgreSQL will require a lot of time and resources.
  3. Data migration from MongoDB to PostgreSQL is a painful and time taking process but using a data integration tool like Hevo can perform this process with no effort and no time.
Visit our Website to Explore Hevo

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 with a hassle-free experience.

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

Frequently Asked Questions

Q. What is the ETL tool used with MongoDB?

Hevo Data. Hevo Data is a cloud-based ETL tool that supports MongoDB as a connector. With Hevo, users can move data from MongoDB to a variety of other destinations of their choice, including data warehouses and databases.

Q. When to use MongoDB instead of PostgreSQL?

If your focus is on modern transactional and analytical applications dealing with rapidly evolving, multi-structured data, MongoDB emerges as the preferred option. Conversely, if your requirements align with a SQL database, PostgreSQL stands out as an excellent selection.

mm
Principal Frontend Engineer, Hevo Data

With over a decade of experience, Suraj has played a crucial role in architecting and developing core frontend modules for Hevo. His expertise lies in building scalable UI solutions, collaborating across teams, and contributing to the open-source community, showcasing a deep commitment to innovation in the tech industry.

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