How To Migrate Data From MongoDB To BigQuery: 2 Easy Methods

MongoDB is a popular NoSQL database that requires data to be modeled in JSON format. If your application’s data model has a natural fit to MongoDB’s recommended data model, it can provide good performance, flexibility, and scalability for transaction types of workloads.

However, due to a few restrictions that you can face while analyzing data, it is highly recommended to stream data from MongoDB to BigQuery or any other data warehouse.

MongoDB doesn’t have proper join, getting data from other systems to MongoDB will be difficult, and it also has no native support for SQL. MongoDB’s aggregation framework is not as easy to draft complex analytics logic as in SQL.

The article provides steps to migrate data from MongoDB to BigQuery. It also talks about Hevo Data, making it easier to replicate data. Therefore, without any further ado, let’s start learning about this MongoDB to BigQuery ETL.

What is MongoDB?

MongoDB is a popular NoSQL database management system known for its flexibility, scalability, and ease of use. It stores data in flexible, JSON-like documents, making it suitable for handling a variety of data types and structures.

MongoDB is commonly used in modern web applications, data analytics, real-time processing, and other scenarios where flexibility and scalability are essential.

Key Features of MongoDB

MongoDB has a variety of unique features that make it a better alternative than other standard databases. Some of these characteristics are as follows:

• Horizontal Scalability: MongoDB’s sharding allows for this. Sharding is the process of sharing data over multiple servers. The Shard Key is used to partition a large amount of data into data chunks, which are then evenly distributed among Shards across several Physical Servers.
• Index-based Document: Every field in the Document is indexed with Primary and Secondary Indices in a MongoDB database, making it easy to access data from the pool.
• Database with No Schemas: A database with no schemas maintains a variety of documents in a single collection (the equivalent of a table). To put it another way, a single MongoDB collection can include several documents, each with its own set of Fields, Content, and Size. Unlike Relational Databases, there is no requirement that one document is equivalent to another. Because of this functionality, MongoDB gives users a lot of flexibility.
• Replication: MongoDB ensures data availability by creating multiple copies of the data and transferring them to a second server, allowing the data to be retrieved even if one server fails.

Also, take a look at the best real-world use cases of MongoDB to get a deeper understanding of how you can efficiently work with your data.

What is BigQuery?

BigQuery is a fully managed, serverless data warehouse and analytics platform provided by Google Cloud. It is designed to handle large-scale data analytics workloads and allows users to run SQL-like queries against multi-terabyte datasets in a matter of seconds.

BigQuery supports real-time data streaming for analysis, integrates with other Google Cloud services, and offers advanced features like machine learning integration, data visualization, and data sharing capabilities.

Learn how you can prepare your data for BigQuery to easily load your data into your BigQuery destination.

Key Features of Google BigQuery

• Multi-Cloud Functionality: BigQuery is an analytics solution that offers data analytics solutions across multiple cloud platforms. The USP of BigQuery is that it provides a novel way of analyzing data in multiple clouds without costing an arm and a leg. 
• Built-in ML Integration: BigQuery ML is used to design and execute ML models with simple SQL queries in BigQuery. Before BigQuery ML was introduced, developers needed ML-specific knowledge and programming skills to build models. 
• Automated Data Transfer: You can automate the movement of data to BigQuery regularly. Analytics teams can easily schedule data movement without any code using ETL tools like Hevo.
• Free access: Google offers a BigQuery sandbox where you can experience the cloud console and BigQuery without any commitment. You don’t have to create a billing account or even provide credit card details. 

Prerequisites

• MongoDB Atlas cluster with data
• BigQuery dataset in your GCP project
• Google Cloud Platform account with BigQuery access
• A Hevo account (free or paid)
• Access to Confluent Cloud/Platform
• MongoDB Source & BigQuery Sink connectors
• GCP service account key with BigQuery permissions

Method 1: Using Hevo Data to Automatic Stream Data from MongoDB to BigQuery

Step 1.1: Configure MongoDB as your Source

Step 1.2: Configure BigQuery as your Destination

By following the above-mentioned steps, you will have successfully completed MongoDB BigQuery replication.

With continuous Real-Time data movement, Hevo allows you to combine MongoDB data with your other data sources and seamlessly load it to BigQuery with a no-code, easy-to-setup interface.

Method 2: Using Confluent Connectors

1. Set Up MongoDB Atlas

• Create a cluster & load data
• Whitelist IP & get connection string

2. Set Up Confluent

• Create Kafka cluster (with Connect & Schema Registry)
• Install MongoDB Source & BigQuery Sink connectors

3. Deploy MongoDB Source Connector

jsonCopyEdit{
  "connector.class": "com.mongodb.kafka.connect.MongoSourceConnector",
  "connection.uri": "mongodb+srv://<username>:<password>@...",
  "database": "sample_mflix",
  "collection": "movies",
  "output.format.value": "json",
  "topic.prefix": "mongo."
}

4. Create BigQuery Dataset

• In GCP Console, create a dataset
• Set up service account & download JSON key

5. Deploy BigQuery Sink Connector

jsonCopyEdit{<br>  "connector.class": "com.wepay.kafka.connect.bigquery.BigQuerySinkConnector",<br>  "topics": "mongo.sample_mflix.movies",<br>  "project": "<gcp-project-id>",<br>  "datasets": "default=<dataset-name>",<br>  "autoCreateTables": "true"<br>}

6. Test the Pipeline

Insert docs in MongoDB → View in BigQuery

As a bonus, you can also connect MongoDB to BigQuery using Google Cloud Dataflow for more customized ETL pipelines. Here’s a detailed guide to help you get started.

MongoDB to BigQuery: Benefits & Use Cases

Benefits of Migrating Data from MongoDB to BigQuery

• Enhanced Analytics: BigQuery provides powerful, real-time analytics capabilities that can handle large-scale data with ease, enabling deeper insights and faster decision-making than MongoDB alone.
• Seamless Integration: BigQuery integrates smoothly with Google’s data ecosystem, allowing you to connect with other tools like Google Data Studio, Google Sheets, and Looker for more advanced data visualization and reporting.
• Scalability and Speed: With BigQuery’s serverless, highly scalable architecture, you can manage and analyze large datasets more efficiently, without worrying about infrastructure limitations.

Use Cases of Migrating Data from MongoDB to BigQuery

• Data warehousing: By streaming data from MongoDB and merging it with data from other sources, businesses may create a cloud data warehouse on top of BigQuery, enabling corporate reporting and dashboards.
• Machine Learning: Streaming data from production MongoDB databases may be utilized to train ML models using BigQuery ML’s comprehensive machine learning features.
• Cloud migration: By gradually streaming data, move analytics from on-premises MongoDB to Google Cloud’s analytics and storage services.

Are you looking for a method to Stream Data from MongoDB Atlas to BigQuery? Check out this article to perform the same in just 2 steps!

Conclusion

This blog makes migrating from MongoDB to BigQuery an easy everyday task for you! The methods discussed in this blog can be applied so that business data in MongoDB and BigQuery can be integrated without any hassle through a smooth transition, with no data loss or inconsistencies.

Sign up for a 14-day free trial with Hevo Data to streamline your migration process and leverage multiple connectors, such as MongoDB and BigQuery, for real-time analysis!

FAQ on MongoDB To BigQuery