Amazon Redshift now scales data ingestion automatically with concurrency scaling for batch workloads

[Amazon Redshift ](https://aws.amazon.com/redshift/)now extends concurrency scaling to support high-volume data ingestion workloads, enabling [concurrency scaling](https://docs.aws.amazon.com/redshift/latest/dg/concurrency-scaling.html) for [Amazon Redshift COPY](https://docs.aws.amazon.com/redshift/latest/dg/r%5FCOPY.html) queries from [Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html). This means your data pipelines no longer have to choose between ingestion speed and query performance—even during peak demand. Organizations running time-sensitive data operations—real-time analytics, continuous ETL, or high-frequency reporting—often face ingestion bottlenecks during traffic spikes. Until now, concurrency scaling supported read queries, but write-heavy workloads could still experience resource contention with concurrent queries. With this launch, Amazon Redshift automatically provisions additional compute capacity to absorb burstiness in ingestion workloads, delivering: This feature is generally available across all AWS commercial regions and AWS GovCloud (US) regions for both Amazon Redshift Serverless and provisioned data warehouses. No migration or configuration changes are required — enable concurrency scaling and your ingestion workloads will benefit immediately. To learn more, visit the [Amazon Redshift concurrency scaling documentation](https://docs.aws.amazon.com/redshift/latest/dg/concurrency-scaling.html).