As organizations scale and their data needs grow more complex, many find themselves at a crossroads between the flexibility of NoSQL databases like MongoDB and the analytical power of cloud data warehouses such as Google BigQuery. This transition from MongoDB to BigQuery is more than just a change in storage – it's a shift in how data is structured, queried, and utilized for business intelligence. In this article, we'll explore two methods for accomplishing this migration: using Airbyte, an open-source data integration platform, and a manual approach using Google Cloud's native tools.

Benefits of moving to BigQuery from MongoDB

1. Fast analytics without impacting operational workloads: BigQuery is designed for fast and efficient analytics. By creating a copy of your operational data in BigQuery, you can execute complex analytical queries against that copy, without impacting your operational workloads.
2. Creating a single source of truth and optimize reporting workflows: It can be time-consuming and challenging for analysts to work with multiple platforms. Combining data from multiple systems into a centralized data warehouse such as BigQuery reduces this workload by serving as a single source of truth.
3. Improved security: Replicating data out of MongoDB into an analytical system such as BigQuery removes the need to grant permissions to data analysts on operational systems, which can improve security.
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Methods to Move Data From Mongodb to Bigquery

• Method 1: Connecting Mongodb to Bigquery using Airbyte.
• Method 2: Connecting Mongdb to Bigquery manually.

Method 1: Connecting Mongodb to Bigquery using Airbyte.

Prerequisites

1. Clever Cloud - hosting of the MongoDB database.
2. mongosh - CLI client to access a MongoDB database.
3. Google cloud - to create a BigQuery data warehouse.
4. Airbyte cloud - a data integration tool that is used to replicate and synchronize data between MongoDB and BigQuery. 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 choose Next.

You should now have a new MongoDB database and the details to connect to it. To connect to the instance, copy the mongo CLI connection command from the Clever Cloud add-on dashboard shown below:

Replace “mongo” with “mongosh” before executing the command in your terminal, as demonstrated below:

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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,  download a sample “books” collection from a publicly available dataset. Then execute the following command:

mongoimport --host=n1-c2-mongodb-clevercloud-customers.services.clever-cloud.com --port=27017 --username= --password= --db= –collection=books books.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 BigQuery

To set up a BigQuery Airbyte destination, you need first to create a BigQuery dataset. Login in to your Google cloud dashboard. From the Welcome page, and click on the Run a query in BigQuery button as shown below:

From the 3-dot menu of your cloud project, choose the only option to create a dataset.

For the Dataset ID, choose a descriptive name like mongodb_dataset. Only alphanumeric names are allowed as dataset ID names.

Choose the dataset location from the menu and click Create Dataset. You should now be able to see a newly created dataset. Take note of the Dataset ID you just entered since you will need it to set up BigQuery as an Airbyte Destination.

Get a copy of the account keys to access our BigQuery project. Choose API & Services from the Quick Access menu in your cloud dashboard.

From the credentials menu, choose to create a new Service account.

Pick a name for your Service account and click Create and continue.

Next, you need to specify what resources can be accessed from this service account. From the available roles,  BigQuery Data Editor and BigQuery Job User should be sufficient. Alternatively BigQuery Admin should work (and is shown in the image below), but more specific security roles should be used in production systems.

Click Done, and you should see a new service account.

Choose this newly created service account email and add a new key by clicking the Add Key button.

From the Create private key pop-up, choose JSON.

Once you select Create, a new private key file will be downloaded to your system. The final step involves creating a new Airbyte destination.

Step 4: Configure a BigQuery destination connector

From your Airbyte dashboard, choose Destinations, select New Destination, and pick BigQuery from the available options. You will then see a UI similar to the following:

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Complete the fields as shown above, and for Service Account Key JSON you should copy the entire contents of the JSON file that you downloaded from BigQuery.

Step 5: Set up an Airbyte Connection from MongoDB to BigQuery

The final step for this tutorial involves building a connection between our newly setup SQL Server and our BigQuery warehouse. To achieve this, go to Connections and choose to set up a New connection. Select the source and destination that you just created, and Airbyte will show you the tables (referred to as streams in Airbyte) that can be synced.

Airbyte has detected the books collections that you imported into MongoDB. 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 the Replication frequency, specify the interval between sync runs. 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.

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Let’s head over to our dataset dashboard on BigQuery.

Choose the books table to preview its contents and schema.

Airbyte Cloud correctly moved 431 MongoDB documents.

That’s it for the tutorial - you now have a data pipeline that automatically transfers data from MongoDB to Google BigQuery. You can now use BigQuery's powerful analytics capabilities to do complex analysis of your data.

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Method 2: Connecting Mongdb to Bigquery manually.

Moving data from MongoDB to Google BigQuery manually can be a bit challenging, but it's definitely possible. Here's a step-by-step guide to help you accomplish this task:

Step 1: Export Data from MongoDB

Before you can move your data, you need to export it from MongoDB. You can use the mongoexport command-line tool to export your data in a JSON or CSV format.

1. Open your terminal or command prompt.
2. Use the mongoexport command to export your collection. For example, to export a collection named myCollection from a database named myDatabase to a JSON file, you would use:
   mongoexport --db=myDatabase --collection=myCollection --out=myCollection.json
3. To export to a CSV file, you would specify the fields and use the --type=csv option:
   mongoexport --db=myDatabase --collection=myCollection --type=csv --fields=field1,field2 --out=myCollection.csv
4. Once the export command completes, you should have a myCollection.json or myCollection.csv file with your data.

Step 2: Prepare Your Data for BigQuery

BigQuery expects the data to be in a certain format, especially if you're using JSON. Make sure your exported data conforms to the following:

• JSON file should contain newline-delimited JSON (NDJSON).
• The structure of the data should match the BigQuery table schema you plan to use.

If necessary, you can write a script to transform your data into the correct format or structure.

Step 3: Upload Your Data to Google Cloud Storage

BigQuery can load data directly from Google Cloud Storage. You need to upload your exported files there first.

Create a Google Cloud Storage bucket if you don't already have one:
gsutil mb gs://your-bucket-name

Upload your data file to the bucket:
gsutil cp myCollection.json gs://your-bucket-name

Step 4: Create a BigQuery Dataset and Table

1. Go to the BigQuery web UI in your Google Cloud Platform console.
2. Click on your project name, then click on "Create dataset."
3. Fill in the details for your dataset and click "Create dataset."
4. Inside the dataset, click on "Create table."
5. Set the "Create table from" option to "Google Cloud Storage" and enter the path to your file (gs://your-bucket-name/myCollection.json).
6. Specify the table name and table type (Native table).
7. In the Schema section, either manually input the schema that matches your MongoDB data or use the "Auto-detect" option if your data is well structured.
8. Click "Create table."

Step 5: Load Data into BigQuery

If you did not load the data in the previous step while creating the table, you can manually load it as follows:

1. In the BigQuery UI, navigate to your dataset and table.
2. Click on your table, then click on the "Load data" button.
3. Choose the source format and select the file from Google Cloud Storage.
4. Configure the schema settings as needed.
5. Click "Start job" to begin loading your data into BigQuery.

Step 6: Verify Data Integrity

After loading, it's crucial to verify that your data has been correctly imported:

1. Run some queries in the BigQuery UI to check if the data looks correct.
2. Compare the number of records and some sample data with your original MongoDB data to ensure completeness and accuracy.

Step 7: Clean Up

Once your data is successfully moved and verified, you might want to clean up to avoid unnecessary storage costs:

Delete the exported data from Google Cloud Storage:
gsutil rm gs://your-bucket-name/myCollection.json

If you created any temporary files or scripts for transforming your data, consider archiving or deleting them if they are no longer needed.

By following these steps, you should be able to move your data from MongoDB to Google BigQuery manually. Remember to handle your data securely and comply with any relevant data protection regulations during the process.

Ready to Migrate MongoDB to BigQuery? Which method should you choose?

Data synchronization

Manual: Requires custom scripts for incremental updates, which can be complex to maintain and error-prone.

Airbyte: Supports both incremental and full refreshes out of the box, accommodating databases of any size. This flexibility ensures efficient data transfer and reduces unnecessary strain on resources.

Automation

Manual: Relies on external schedulers like cron jobs, which may lack robust error handling and reporting.

Airbyte: Offers built-in scheduling for full-refresh, incremental, and log-based Change Data Capture (CDC) replications. This feature provides a seamless, automated data pipeline that keeps your BigQuery instance up-to-date with minimal intervention.

Security

Manual: Requires careful implementation of security best practices, which can be overlooked or improperly configured.

Airbyte: Provides secure connections to databases using industry-standard methods like SSL/TLS and SSH tunnels, ensuring data integrity and confidentiality during transfer.

Data transformation

Manual: Necessitates separate ETL processes, often requiring additional tools or complex scripts.

Airbyte: Integrates with dbt, allowing you to add custom transformations directly within the application. This streamlines the ETL process and provides a unified platform for both data extraction and transformation.

Error handling

Manual: Typically requires custom logging implementations, which may not capture all necessary details for troubleshooting.

Airbyte: Logs all errors in full detail, providing comprehensive information to help you understand and resolve issues quickly. This feature is crucial for maintaining data integrity and ensuring smooth operations.

Scalability

Manual: Can become unmanageable as data volume and complexity increase, often requiring significant refactoring.

Airbyte: Designed to handle databases of any size, with the ability to scale horizontally as your data needs grow.

Conclusion

Migrating data from MongoDB to BigQuery represents a significant step in evolving your data infrastructure. While manual methods offer granular control, Airbyte emerges as the superior solution for most scenarios. Its automated pipelines, robust security features, and built-in transformation capabilities streamline the migration process, reducing complexity and potential errors. As data volumes grow and real-time analytics become increasingly critical, tools like Airbyte will play a pivotal role in ensuring seamless data integration. The choice between manual and automated approaches depends on your specific needs, but for organizations seeking efficiency, scalability, and ease of use, Airbyte provides a compelling path forward in the MongoDB to BigQuery migration journey.

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 wish to check out other Airbyte tutorials and Airbyte’s blog!