Posted On: Nov 24, 2021

Amazon Redshift data sharing allows you to share live, transactionally consistent data across different Redshift clusters without the complexity and delays associated with data copies and data movement. Data sharing now adds several performance enhancements including result caching, and concurrency scaling allowing you to support broader set of analytics applications and meet critical performance SLAs when querying shared data.

Data sharing allows you to rapidly onboard new analytics workloads and provision them with flexible compute resources to meet individual workload-specific performance SLAs. With the new performance enhancements, data sharing makes it easier to support analytics that require low latency and high concurrency such as dash boarding applications using optimizations that minimize the amount of data that need to be accessed by the consumer clusters. Result caching helps with reducing query execution time and improve system performance by caching the results of certain types of queries in memory. When a user submits a query, Amazon Redshift checks the results cache for a valid, cached copy of the query results making it possible to offer sub-second response times. With the concurrency scaling feature, you can support virtually unlimited concurrent users and concurrent queries on shared data, with consistently fast query performance.

The new performance enhancements are available in all regions where data sharing is available. Learn more about data sharing capability in feature page and refer to documentation. Refer to enabling workload isolation and supporting multi-tenancy and data as a service to learn more about use cases.