Announcing Amazon Redshift Serverless with AI-driven scaling and optimizations (Preview)


Today, Amazon Redshift Serverless introduces a preview of the next generation of artificial intelligence (AI)–driven scaling and optimization in cloud data warehousing. Amazon Redshift Serverless uses AI techniques to scale automatically with workload changes across all key dimensions—such as data volume changes, concurrent users, and query complexity—to meet and maintain your price performance targets.  Internal tests demonstrate that these optimizations can give you up to 10x better price performance for variable workloads without manual intervention.

With these new AI-driven scaling and optimizations, Amazon Redshift Serverless learns your workload patterns based on dimensions like query complexity and data volumes. It continually adjusts resources throughout the day to apply tailored performance optimizations, and it automatically and proactively adjusts the capacity based on actual workload needs. Furthermore, Amazon Redshift Serverless introduces system-wide new AI-enhanced optimizations and forecasting that go beyond the current, already leading self-tuning capabilities of Amazon Redshift, such as automatic materialized views and sort orders. For instance, it has an ML-enhanced sorting technique that automatically organizes data beyond what traditional encodings can achieve today. You can use a price-performance slider to set your desired price-performance target for your workload.

AI-driven scaling and optimization is available in preview in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), Europe (Ireland), and Europe (Stockholm).

To get started, create Amazon Redshift Serverless using the PREVIEW_2023 track. Note that preview features are provided primarily for evaluation and testing purposes and should not be used in production systems. For preview terms and conditions, see AWS Service Terms: Beta Service Participation.

For more information, see the following list of resources:



Source link

About The Author

Scroll to Top