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 webs, mobile, and analytics applications use PostgreSQL as the primary data warehouse or data store.
MongoDB is a database that powers crucial applications and systems for global businesses. Designed for developers and specializing in the areas of open source, software development, and databases, it offers functionality such as horizontal scaling, automatic failover, and the capability to assign data to a location.
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1. Open your PostgreSQL database and create a new user with the necessary permissions to access the data you want to replicate.
2. Obtain the hostname or IP address of your PostgreSQL server and the port number it is listening on.
3. Create a new database in PostgreSQL that will be used to store the replicated data.
4. Obtain the name of the database you just created.
5. In Airbyte, navigate to the PostgreSQL source connector and click on "Create Connection".
6. Enter a name for your connection and fill in the required fields, including the hostname or IP address, port number, database name, username, and password.
7. Test the connection to ensure that Airbyte can successfully connect to your PostgreSQL database.
8. Select the tables or views you want to replicate and configure any necessary settings, such as the replication frequency and the replication method.
9. Save your configuration and start the replication process.
10. Monitor the replication process to ensure that it is running smoothly and troubleshoot any issues that arise.
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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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Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
- set up Postgres as a source connector (using Auth, or usually an API key)
- set up MongoDB as a destination connector
- define which data you want to transfer and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.
This tutorial’s purpose is to show you how.
What is Postgres
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 webs, mobile, and analytics applications use PostgreSQL as the primary data warehouse or data store.
What is MongoDB
MongoDB is a database that powers crucial applications and systems for global businesses. Designed for developers and specializing in the areas of open source, software development, and databases, it offers functionality such as horizontal scaling, automatic failover, and the capability to assign data to a location.
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Prerequisites
- A Postgres account to transfer your customer data automatically from.
- A MongoDB account.
- An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.
Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Postgres and MongoDB, for seamless data migration.
When using Airbyte to move data from Postgres to MongoDB, it extracts data from Postgres using the source connector, converts it into a format MongoDB can ingest using the provided schema, and then loads it into MongoDB via the destination connector. This allows businesses to leverage their Postgres data for advanced analytics and insights within MongoDB, simplifying the ETL process and saving significant time and resources.
Methods to Move Data From Postgresql to Mongodb
- Method 1: Connecting Postgresql to Mongodb using Airbyte.
- Method 2: Connecting Postgresql to Mongodb manually.
Method 1: Connecting Postgresql to Mongodb using Airbyte.
Step 1: Set up Postgres as a source connector
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1. Open your PostgreSQL database and create a new user with the necessary permissions to access the data you want to replicate.
2. Obtain the hostname or IP address of your PostgreSQL server and the port number it is listening on.
3. Create a new database in PostgreSQL that will be used to store the replicated data.
4. Obtain the name of the database you just created.
5. In Airbyte, navigate to the PostgreSQL source connector and click on "Create Connection".
6. Enter a name for your connection and fill in the required fields, including the hostname or IP address, port number, database name, username, and password.
7. Test the connection to ensure that Airbyte can successfully connect to your PostgreSQL database.
8. Select the tables or views you want to replicate and configure any necessary settings, such as the replication frequency and the replication method.
9. Save your configuration and start the replication process.
10. Monitor the replication process to ensure that it is running smoothly and troubleshoot any issues that arise.
Step 2: Set up MongoDB as a destination connector
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Step 3: Set up a connection to sync your Postgres data to MongoDB
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Once you've successfully connected Postgres as a data source and MongoDB as a destination in Airbyte, you can set up a data pipeline between them with the following steps:
- Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
- Choose your source: Select Postgres from the dropdown list of your configured sources.
- Select your destination: Choose MongoDB from the dropdown list of your configured destinations.
- Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
- Select the data to sync: Choose the specific Postgres objects you want to import data from towards MongoDB. You can sync all data or select specific tables and fields.
- Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
- Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
- Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Postgres to MongoDB according to your settings.
Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your MongoDB data warehouse is always up-to-date with your Postgres data.
Method 2: Connecting Postgresql to Mongodb manually.
Moving data from PostgreSQL to MongoDB manually can be a manual process that involves extracting data from PostgreSQL, transforming it into a format that MongoDB can ingest, and then importing it into MongoDB. Here's a step-by-step guide to accomplish this task:
Step 1: Export Data from PostgreSQL
- Identify the Data to Export: Pick the tables or data you need to move from PostgreSQL to MongoDB.
- Connect to PostgreSQL: Use the psql command-line tool or a PostgreSQL client to connect to your database.
psql -h hostname -p port -U username -d databasename - Export Data: Use the COPY command in PostgreSQL to export the data to a CSV file.
\COPY tablename TO 'path_to_csv_file.csv' WITH CSV HEADER; - Repeat this step for each table you want to export.
Step 2: Transform Data (If Necessary)
- Analyze Data: Look at the data in the CSV files and decide how you want to structure it in MongoDB, which is document-oriented.
- Transform Data: Write a script or use a spreadsheet program to transform the relational data into JSON documents. This might involve:some text
- Combining data from multiple tables into a single document (denormalization).
- Converting foreign keys into nested documents or arrays.
- Changing date and time formats to ISO 8601 format, which MongoDB uses.
Step 3: Prepare MongoDB
- Install MongoDB: If not already installed, download and install MongoDB from the official website.
- Start MongoDB: Run the MongoDB server (mongod) on your system.
- Create a Database & Collections: Connect to MongoDB using the mongo shell and create a new database and collections.
use newdatabase
db.createCollection("newcollection")
- Repeat the collection creation for each type of data you are importing.
Step 4: Import Data into MongoDB
- Convert CSV to JSON: Use a conversion tool or write a script to convert your CSV files to JSON format. Make sure the JSON structure matches the MongoDB collections you've created.
- Import JSON Data: Use the mongoimport tool to import the JSON files into the appropriate MongoDB collections.
mongoimport --db newdatabase --collection newcollection --file 'path_to_json_file.json' - Repeat this step for each JSON file corresponding to a MongoDB collection.
Step 5: Verify Data Integrity
- Check Counts: Compare the number of records in PostgreSQL and MongoDB to ensure they match.
- Sample Data: Query a few documents from MongoDB and compare them with the original data in PostgreSQL to verify that the transformation and import processes worked correctly.
Step 6: Clean Up
- Backup: Make sure to back up your original PostgreSQL data before decommissioning any servers or services.
- Remove Temporary Files: Delete any intermediate CSV or JSON files if they are no longer needed.
Tips:
- Always perform these operations in a test environment before moving to production.
- Consider indexing your MongoDB collections after the import to optimize query performance.
- Test your application against the new MongoDB data to ensure compatibility.
- Monitor MongoDB performance and adjust the schema or indexing strategy as needed.
Remember that the complexity of this process can vary greatly depending on the structure and size of your data, and it might require custom scripting to handle complex transformations.
Use Cases to transfer your Postgres data to MongoDB
Integrating data from Postgres to MongoDB provides several benefits. Here are a few use cases:
- Advanced Analytics: MongoDB’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Postgres data, extracting insights that wouldn't be possible within Postgres alone.
- Data Consolidation: If you're using multiple other sources along with Postgres, syncing to MongoDB allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
- Historical Data Analysis: Postgres has limits on historical data. Syncing data to MongoDB allows for long-term data retention and analysis of historical trends over time.
- Data Security and Compliance: MongoDB provides robust data security features. Syncing Postgres data to MongoDB ensures your data is secured and allows for advanced data governance and compliance management.
- Scalability: MongoDB can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Postgres data.
- Data Science and Machine Learning: By having Postgres data in MongoDB, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
- Reporting and Visualization: While Postgres provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to MongoDB, providing more advanced business intelligence options. If you have a Postgres table that needs to be converted to a MongoDB table, Airbyte can do that automatically.
Wrapping Up
To summarize, this tutorial has shown you how to:
- Configure a Postgres account as an Airbyte data source connector.
- Configure MongoDB as a data destination connector.
- Create an Airbyte data pipeline that will automatically be moving data directly from Postgres to MongoDB after you set a schedule
For further insights, consider exploring an informative article detailing the migration process from MongoDB to Postgres.
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!
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
PostgreSQL gives access to a wide range of data types, including:
1. Numeric data types: This includes integers, floating-point numbers, and decimal numbers.
2. Character data types: This includes strings, text, and character arrays.
3. Date and time data types: This includes dates, times, and timestamps.
4. Boolean data types: This includes true/false values.
5. Network address data types: This includes IP addresses and MAC addresses.
6. Geometric data types: This includes points, lines, and polygons.
7. Array data types: This includes arrays of any of the above data types.
8. JSON and JSONB data types: This includes JSON objects and arrays.
9. XML data types: This includes XML documents.
10. Composite data types: This includes user-defined data types that can contain multiple fields of different data types.
Overall, PostgreSQL's API provides access to a wide range of data types, making it a versatile and powerful tool for data management and analysis.