Copy data
from Google BigQuery to Redshift
Ingest data from Google BigQuery into Redshift with no code required. Extend if needed with custom code.
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.
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
Google BigQuery & 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.