How to Sync Data from MongoDB to PostgreSQL?: 5 Easy Steps
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FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
MongoDB is a popular open-source NoSQL database that stores data in a flexible, document-based format. It is designed to handle large volumes of unstructured data and is highly scalable, making it a popular choice for modern web applications. MongoDB uses a JSON-like format to store data, which allows for easy integration with web applications and APIs. It also supports dynamic queries, indexing, and aggregation, making it a powerful tool for data analysis. MongoDB is widely used in industries such as finance, healthcare, and e-commerce, and is known for its ease of use and flexibility.
MongoDB gives access to a wide range of data types, including:
1. Documents: MongoDB stores data in the form of documents, which are similar to JSON objects. Each document contains a set of key-value pairs that represent the data.
2. Collections: A collection is a group of related documents that are stored together in MongoDB. Collections can be thought of as tables in a relational database.
3. Indexes: MongoDB supports various types of indexes, including single-field, compound, and geospatial indexes. Indexes are used to improve query performance.
4. GridFS: MongoDB's GridFS is a specification for storing and retrieving large files, such as images and videos, in MongoDB.
5. Aggregation: MongoDB's aggregation framework provides a way to perform complex data analysis operations, such as grouping, filtering, and sorting, on large datasets.
6. Transactions: MongoDB supports multi-document transactions, which allow multiple operations to be performed atomically.
7. Change streams: MongoDB's change streams provide a way to monitor changes to data in real-time, allowing applications to react to changes as they occur.
Overall, MongoDB provides access to a flexible and powerful data model that can handle a wide range of data types and use cases.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
How to Sync Data from MongoDB to PostgreSQL?: 5 Easy Steps
MongoDB is a popular open-source NoSQL database that stores data in a flexible, document-based format. It is designed to handle large volumes of unstructured data and is highly scalable, making it a popular choice for modern web applications. MongoDB uses a JSON-like format to store data, which allows for easy integration with web applications and APIs. It also supports dynamic queries, indexing, and aggregation, making it a powerful tool for data analysis. MongoDB is widely used in industries such as finance, healthcare, and e-commerce, and is known for its ease of use and flexibility.
An object-relational database management system, PostgreSQL is able to handle a wide range of workloads, supports multiple standards, and is cross-platform, running on numerous operating systems including Microsoft Windows, Solaris, Linux, and FreeBSD. It is highly extensible, and supports more than 12 procedural languages, Spatial data support, Gin and GIST Indexes, and more. Many web, mobile, and analytics applications use PostgreSQL as the primary data warehouse or data store.
1. First, you need to have a MongoDB instance running and accessible from the internet. You will also need to have the necessary credentials to access the database.
2. In the Airbyte dashboard, click on "Sources" and then click on "New Source."
3. Select "MongoDB" from the list of available sources.
4. In the "Connection Configuration" section, enter the following information:
- Host: The hostname or IP address of your MongoDB instance.
- Port: The port number on which your MongoDB instance is running.
- Username: The username you use to access your MongoDB instance.
- Password: The password you use to access your MongoDB instance.
- Authentication Database: The name of the database where your authentication credentials are stored.
5. Click on "Test Connection" to ensure that Airbyte can connect to your MongoDB instance.
6. If the connection is successful, click on "Save" to save your MongoDB source configuration.
7. You can now create a new pipeline and select your MongoDB source as the input. You can then configure the pipeline to transform and load your data into your desired destination.
With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.
We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!
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MongoDB is a distributed database that is built for modern transactional and analytical applications and may be used for rapidly changing, multi-structured data. On the other hand, PostgreSQL is an SQL database that has all of the features that you require from a relational database.
Choosing one or the other between MongoDB and PostgreSQL may not be your only option – in-fact, because each database has different strengths you may wish to use them side-by-side. If this is your case, then you may need to sync data between them.
Airbyte offers an efficient way to transfer data from MongoDB to PostgreSQL, simplifying the process without extensive programming. Alternatively, manual integration can be employed for more customized control. While both methods ensure successful data migration, manual integration typically requires a greater investment in resources and expertise, as we'll explore in more detail later in this article.
Overview of MongoDB and PostgreSQL
Understanding MongoDB
MongoDB is a popular NoSQL database management system known for its flexible, document-oriented data model. It stores data in JSON-like documents, making it easy to work with dynamic schemas and accommodate evolving data requirements. MongoDB is widely used for its scalability, high performance, and ease of use in modern application development, especially when strict schema enforcement is not required.
Understanding PostgreSQL
Postgres, also known as PostgreSQL, is a powerful open-source relational database management system. It offers robust features such as ACID compliance, support for complex queries, extensibility through user-defined functions, and data integrity mechanisms. Postgres is highly favored for its reliability, data integrity, and SQL support, making it suitable for a wide range of applications.
Key Features of MongoDB and Postgres
- MongoDB:
- Schema flexibility with dynamic schemas
- Scalability through horizontal scaling and sharding
- High availability with built-in replication
- Rich query language and indexing support
- Postgres:
- ACID compliance for data consistency
- Support for complex SQL queries and transactions
- Extensibility with user-defined functions and procedural languages
- Data integrity mechanisms such as constraints and foreign keys
Enhance your understanding by exploring an insightful article on MongoDB vs PostgreSQL to compare and discover the best fit for your database needs!
The Need for Data Synchronization
Importance of Data Consistency
Data consistency is crucial for ensuring accuracy and reliability in business operations. Inconsistent data across multiple systems can lead to errors, misinformation, and inefficiencies. Synchronizing data between MongoDB and Postgres helps maintain consistency and coherence across the entire data ecosystem.
Challenges of Data Silos
Data silos occur when data is stored in isolated systems or departments, hindering collaboration and data analysis. Integrating MongoDB and Postgres enables seamless data sharing and collaboration, breaking down silos and facilitating a unified view of organizational data.
Role of Data Synchronization in Business Operations
Data synchronization plays a vital role in enabling real-time decision-making, improving operational efficiency, and enhancing customer experiences. By synchronizing data between MongoDB and Postgres, organizations can leverage timely and accurate insights to drive strategic initiatives and gain a competitive edge.
Key Benefits of Data Synchronization
- Ensures Data Consistency: Data synchronization ensures that data remains consistent across different systems and databases. By keeping data in sync between MongoDB and Postgres, organizations can avoid discrepancies and maintain data integrity throughout their operations.
- Enables Real-Time Decision Making: Real-time data synchronization enables organizations to access up-to-date information for timely decision-making. By synchronizing data between MongoDB and Postgres in real-time or near-real-time, businesses can respond quickly to changing market conditions and make informed decisions.
- Facilitates Data Integration Across Systems: Data synchronization facilitates seamless integration of data between MongoDB and Postgres, as well as with other systems and applications. This integration enables data sharing, cross-functional collaboration, and enhanced interoperability across the organization.
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Methods of Loading data from MongoDB to PostgreSQL
Manual Data Transfer Methods:
Manual data transfer methods involve exporting data from MongoDB and importing it into Postgres manually. While this approach is straightforward for small datasets, it becomes cumbersome and error-prone for larger volumes of data.
Introduction to ETL Tools like Airbyte:
ETL (Extract, Transform, Load) tools like Airbyte automate the process of data synchronization from MongoDB to Postgres. Airbyte simplifies data integration by providing a user-friendly interface, pre-built connectors, and automated workflows for seamless data transfer.
Advantages and Disadvantages of Each Method
- Manual Data Transfer:
- Advantages: Simple, low-cost solution for small datasets.
- Disadvantages: Time-consuming, error-prone, not suitable for large datasets.
- ETL Tools like Airbyte:
- Advantages: Automated, scalable, real-time data synchronization, reduced risk of errors.
- Disadvantages: Initial setup may require some technical expertise, cost associated with the tool.
Method 1: How to Load Data from MongoDB to Postgres using Airbyte?
Let's deep dive into the step-by-step guide to explore how to seamlessly transfer data between MongoDB and PostgreSQL using the powerful data integration tool, Airbyte.
Prerequisites
This tutorial makes use of the following tools:
- Clever Cloud - hosting of the MongoDB and PostgreSQL databases.
- mongosh - a CLI client to interface with the MongoDB database.
- Postgres CLI - a terminal-based front-end to PostgreSQL.
- Airbyte cloud - a data integration tool that will be used to replicate and synchronize data between MongoDB and PostgreSQL. Alternatively, you may choose to install Airbyte OSS locally.
Step 1-a: Launch MongoDB
In this section, you will use Clever Cloud to create a MongoDB instance. Once you sign up, choose the option to create an add-on from your personal space.
From the available list of add-ons, choose the MongoDB add-on.
For the instance size, choose the DEV plan, which is free to use.
Enter an add-on name and select a region as shown below, and then click Next.
You should now have a new MongoDB database created with all the details to connect to it. Copy the mongo CLI connection command from the Clever Cloud add-on dashboard shown below
Copy and replace “mongo” with “mongosh” before executing the command in your terminal, as demonstrated below:
You should now be connected to the PRIMARY replica of the MongoDB replica set (indicated by [primary] in the shell).
In the Airbyte connection to MongoDB, you will make use of the URL for the primary replica. This can be retrieved by running rs.isMaster().primary in the MongoDB shell, which will respond with a string in the format of [hostname]:[port]. In our case, the URL returned by this command is is n2-c2-mongodb-clevercloud-customers.services.clever-cloud.com:27017
Step 1-b: Add data to MongoDB (optional)
For this demo, we download and then import a sample restaurant collection using the mongoimport db tool command.
mongoimport --host=n2-c2-mongodb-clevercloud-customers.services.clever-cloud.com --port=27017 --username= --password= --db= –collection=restaurant restaurant.json
Step 2: Configure a MongoDB source connector
Create an Airbyte MongoDB source by choosing sources from your Airbyte dashboard and clicking on the New source button. Then from the list of sources, choose MongoDB, and you should see a UI similar to the following:
To keep this tutorial simple, and for demonstration purposes only, in the above image we have selected a Standalone MongoDB instance. However, you may also consider selecting one of the alternative MongoDB configuration parameters if you wish to have a more resilient connection to your MongoDB cluster.
Enter the Host, Port, Username, DB Name, and Password that were shown earlier in the Clever Cloud MongoDB configuration UI. Then choose Set up source.
Step 3: Launch PostgreSQL
To set up a PostgreSQL database, create a new add-on on your Clever Cloud dashboard, and choose PostgreSQL from the available add-ons
For plan, choose the DEV option, which provides 256 MB for storage.
Give a name to your add-on, and choose a location.
Click on Next once you are satisfied with your configurations, and then Clever Cloud should show you the PostgreSQL database credentials with information that will be required by Airbyte, including host, user, password, and database name.
To connect to our newly created database, copy the Connection URI and provide it as an argument to the psql CLI tool as shown below.
Step 4: Configure a PostgreSQL destination connector
Go to Destinations in your Airbyte Dashboard, choose to Create destination from the list, and choose PostgreSQL. You will then see a UI similar to the following:
Enter in the PostgreSQL parameters that were returned by Clever Cloud, and click Set up destination.
Step 5: Set up an Airbyte connection between MongoDB & PostgreSQL
Go to Connections in your Airbyte dashboard and choose New connection. Select the source and the destination that you just created, at which point you should see a UI similar to the following:
Airbyte has correctly detected the restaurant collection as a stream, and you can choose how it should be replicated to PostgreSQL. For sync mode, choose one from the available modes – for more information you may wish to consult the blog: An overview of Airbyte’s replication modes.
For Replication frequency, specify the interval between sync runs, which can be adjusted directly through the Command Line for more control. Once you are done with the configurations, choose Set up connection and Airbyte will start its first sync. Once complete, you will be able to see how many records were replicated.
Log in to Postgres host to see the replicated data. Note that you must change the search_path according to the DB Name that you specified when you set up the PostgreSQL destination in Airbyte.
You should now be able to view the replicated data using standard SQL commands.
Benefits of using Airbyte for Data Synchronization
- Streamlined Data Pipelines: Airbyte provides pre-built connectors and workflows for seamless data synchronization between MongoDB and Postgres. Its intuitive interface and automation capabilities streamline the process of setting up and managing data pipelines.
- Real-Time Monitoring and Error Handling: Airbyte offers real-time monitoring and error handling features, allowing users to track the status of data sync jobs and troubleshoot any issues promptly. This ensures data integrity and reliability throughout the synchronization process.
- Scalability and Flexibility: Airbyte is designed to scale with the growing needs of organizations, supporting large volumes of data and complex data integration scenarios. Its flexible architecture allows for easy customization and adaptation to changing business requirements.
- User-Friendly Interface: Airbyte's user-friendly interface makes it easy for users of all skill levels to configure, deploy, and monitor data synchronization tasks. With its intuitive design and guided workflows, organizations can accelerate their data integration initiatives without requiring extensive technical expertise.
Method 2: Manual ETL to Move Data from MongoDB to PostgreSQL
Step 1: Evaluate the Existing MongoDB Database
Begin by understanding the structure and size of your MongoDB database. This involves reviewing collections, documents, and data types to get a clear picture of what needs to be migrated.
Step 2: Configure the PostgreSQL Database Target
After installing PostgreSQL, create the necessary databases and tables that match the structure of your MongoDB collections.
Step 3: Export Data from MongoDB
Export your MongoDB data using the mongoexport utility. Choose the appropriate format (JSON or CSV) for your data.
Example Command:
mongoexport --db mydb --collection users --out users.json
Step 4: Prepare the PostgreSQL Schema
- Ensure that the PostgreSQL schema is designed to meet relational data requirements and reflects the MongoDB data structure appropriately.
- Create tables, define data types, and set up constraints based on the exported MongoDB data model.
Step 5: Transform and Clean Data
- Data Transformation: Write scripts or use data transformation tools to transform the exported data into a format compatible with PostgreSQL.
- Flatten Structures: Address nested structures, convert data types, and handle arrays and embedded documents.
- Data Cleaning: Clean the data to align with PostgreSQL schema standards.
Step 6: Load Data into PostgreSQL
Use the COPY command or the psql program to load data into PostgreSQL tables.
Example Command:
COPY users FROM 'path_to_file/users.json' WITH (FORMAT json);
Step 7: Verify the Migrated Data
Compare data between PostgreSQL and MongoDB to ensure accuracy and completeness. Check data counts, key constraints, and sample records to confirm successful migration.
Step 8: Monitor the Performance
Optimize the PostgreSQL database by adjusting setup parameters, creating indexes, and optimizing queries. Keep an eye on database performance and make necessary adjustments to maintain efficiency.
Which migration method to choose?
Airbyte ETL and manual migration both have their advantages when it comes to migrating data from MongoDB to PostgreSQL. Airbyte ETL offers an intuitive interface, pre-built connectors, automatic handling of large datasets, and custom data transformations, making it easy to use and scalable. On the other hand, manual migration provides full control and flexibility. However, it is complex, requires coding and debugging skills, and has a high maintenance burden.
Conclusion
Data synchronization between MongoDB and Postgres is essential for maintaining data consistency and enabling real-time decision-making. Manual data transfer methods are inefficient and error-prone, especially for large datasets. ETL tools like Airbyte offer automated, scalable, and real-time data synchronization capabilities, overcoming the limitations of manual methods.
In summary, in this tutorial you have learned how to:
- Launch a MongoDB database on Clever Cloud, and then how to add some test data to it.
- Configure an Airbyte source connector to read data from MongoDB.
- Launch a PostgreSQL database on Clever Cloud.
- Configure an Airbyte destination connector to send data into PostgreSQL.
- Create an Airbyte connection that replicates data from a MongoDB to Postgres.
With Airbyte, the data integration possibilities are endless, and we look forward to seeing you use it! We invite you to join the conversation on our community Slack Channel, participate in discussions on Airbyte’s discourse, or sign up for our newsletter. You may also be interested in other Airbyte tutorials and Airbyte’s blog!
FAQs (Frequently Asked Questions)
- What is MongoDB to PostgreSQL data synchronization?
MongoDB to Postgres data synchronization refers to the process of transferring data from MongoDB, a NoSQL database, to Postgres, a relational database, while ensuring consistency and integrity between the two databases. - Why is data synchronization between MongoDB and Postgres necessary?
Synchronizing data between MongoDB and Postgres enables organizations to leverage the strengths of both databases. MongoDB excels at handling unstructured data, while Postgres is well-suited for managing relational data. Syncing data ensures seamless integration and accessibility across different database systems. - What are the benefits of data synchronization?
Data synchronization facilitates real-time data updates, enhances data consistency, improves data accessibility, and enables better analytics and reporting. It ensures that data is accurate and up-to-date across all systems, enabling organizations to make informed decisions. - What challenges are commonly encountered in this data synchronization?
Common challenges include compatibility issues between data formats, mapping schemas between the two databases, handling large volumes of data efficiently, and maintaining data consistency during the synchronization process. - Why should I use a tool like Airbyte for this data synchronization?
Airbyte simplifies the synchronization process with its user-friendly interface, pre-built connectors for MongoDB and Postgres, support for data transformation tasks, schema mapping capabilities, and features like incremental sync and error handling. It ensures smooth and efficient data integration between MongoDB and Postgres. - How does Airbyte ensure data security during the synchronization process?
Airbyte prioritizes data security by implementing encryption protocols, access controls, and compliance with industry standards. Users can configure encryption settings and access controls according to their organization's security requirements, ensuring the confidentiality and integrity of their data throughout the synchronization process.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
MongoDB gives access to a wide range of data types, including:
1. Documents: MongoDB stores data in the form of documents, which are similar to JSON objects. Each document contains a set of key-value pairs that represent the data.
2. Collections: A collection is a group of related documents that are stored together in MongoDB. Collections can be thought of as tables in a relational database.
3. Indexes: MongoDB supports various types of indexes, including single-field, compound, and geospatial indexes. Indexes are used to improve query performance.
4. GridFS: MongoDB's GridFS is a specification for storing and retrieving large files, such as images and videos, in MongoDB.
5. Aggregation: MongoDB's aggregation framework provides a way to perform complex data analysis operations, such as grouping, filtering, and sorting, on large datasets.
6. Transactions: MongoDB supports multi-document transactions, which allow multiple operations to be performed atomically.
7. Change streams: MongoDB's change streams provide a way to monitor changes to data in real-time, allowing applications to react to changes as they occur.
Overall, MongoDB provides access to a flexible and powerful data model that can handle a wide range of data types and use cases.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: