Copy data
from MongoDB to Redshift
Ingest data from MongoDB into Redshift 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 Redshift?
Amazon Redshift is a fully-managed data warehouse service in the cloud, designed for large-scale data storage and analysis.
- Scalability
- Redshift can scale from a few hundred gigabytes to a petabyte or more, allowing you to handle large datasets efficiently.
- High Performance
- With columnar storage and parallel query execution, Redshift delivers high performance for complex queries.
- Integrated with AWS
- Seamlessly integrates with other AWS services, providing a comprehensive data ecosystem.
- Cost-effective
- Pay-as-you-go pricing and the ability to pause/resume clusters make Redshift a cost-effective data warehousing solution.
Copy data between
MongoDB & Redshift
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.