Copy data
from MongoDB to Google BigQuery
Ingest data from MongoDB into Google BigQuery with no code required. Extend if needed with custom code.
NoSQL Database
What is MongoDB?
MongoDB is a document-oriented NoSQL database used for high volume data storage, offering high performance and flexibility.
- Flexible Schema
- Document-oriented storage with dynamic schemas, allowing for flexible and scalable data models.
- High Performance
- Designed for high performance with support for indexing, replication, and sharding.
- Scalable
- Easily scales horizontally with built-in sharding for distributed data storage.
- Rich Query Language
- Supports a powerful and expressive query language for data retrieval and manipulation.
Data Warehouse
What is Google BigQuery?
BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data.
- Massive Scalability
- BigQuery effortlessly scales to handle petabytes of data, enabling you to manage large datasets without any performance issues.
- Built-in Machine Learning
- BigQuery ML allows you to create and deploy machine learning models directly within SQL, simplifying the process of incorporating ML into your data analysis.
- Real-time Analytics
- BigQuery's real-time analytics capabilities let you analyze streaming data on the fly, ensuring you have the latest insights at your fingertips.
- Cost-effective Pricing
- With BigQuery's pay-as-you-go pricing model, you only pay for the storage and compute resources you actually use, making it a cost-effective solution for data analysis.
Copy data between
MongoDB & Google BigQuery
Bruin Cloud enables you to copy data between any source and destination.
Build data pipelines faster
Built-in connectors, defined with YAML
Bruin is a code-based platform, meaning that everything you do comes from a Git repo, versioned. All of the data ingestions are defined in code, version controlled in your repo.
- Multiple platforms
- Bruin supports quite a few platforms as built-in connectors. You can ingest data from AWS, Azure, GCP, Snowflake, Notion, and more.
- Built on open-source
- Bruin's ingestion engine is built on ingestr, an open-source data ingestion tool.
- Custom sources & destinations
- Bruin supports pure Python executions, enabling you to build your own data ingestion code.
- Incremental loading
- Bruin supports incremental loading, meaning that you can ingest only the new data, not the entire dataset every time.
Build safer
End-to-end quality in raw data
Bruin's built-in data quality capabilities are designed to ensure that the data you ingest is of the highest quality and always matches with your expectations.
- Built-in quality checks
- Bruin supports built-in quality checks, such as not_null, accepted_values, and more, all ready to be used in all assets.
- Custom quality checks
- Bruin allows you to define custom quality checks in SQL, enabling you to define your own quality standards.
- Templating in quality checks
- Bruin supports templating in quality checks, meaning that you can use variables in your checks, and run checks only for incremental periods.
- Automated alerting
- Failing quality checks will automatically send alerts to the configured channels, ensuring that you are always aware of the data quality issues.