Amazon Aurora zero-ETL Integration with Amazon Redshift

Access transactional data from Aurora in Amazon Redshift within seconds to run near real-time analytics and ML on petabytes of data.

Quickly analyze your transactional data in near real time without having to build and manage ETL pipelines to move transactional data to analytics systems.

Consolidate multiple tables from various Aurora database clusters and replicate your data to one Amazon Redshift data warehouse to run unified analytics across multiple applications and data sources.

Run near real-time analytics on transactional data without having to manage any infrastructure when using both Amazon Aurora Serverless v2 and Amazon Redshift Serverless.

Use Amazon Redshift analytics and ML capabilities to derive insights in near real time from transactional and other data to effectively respond to critical, time-sensitive events. Near real-time analytics can help you get more accurate and timely insights for use cases such as content targeting, optimized gaming experience, data quality monitoring, fraud detection, and customer behavior analysis.

With the Aurora zero-ETL integration with Amazon Redshift, you can use Amazon Redshift capabilities to analyze petabytes of your transactional data consolidated from multiple Aurora database clusters. You can take advantage of the comprehensive analytical capabilities of Amazon Redshift, such as built-in ML, materialized views, data sharing, and federated access to multiple data stores and data lakes. With Amazon Redshift ML, you can run billions of predictions with straightforward SQL commands with native integration into Amazon SageMaker.

Moving data from a transactional database into a central data warehouse often requires building, managing, and operating a complex data pipeline ETL solution. With a zero-ETL integration, you can seamlessly replicate the schema, existing data, and data changes from your Aurora database to a new or existing Amazon Redshift cluster. Zero-ETL integration removes the need for complex data pipeline management.