Today, AWS Clean Rooms announces support for advanced configurations to improve the performance of Spark SQL queries. This launch enables you to customize Spark properties and compute sizes for SQL queries at runtime, offering increased flexibility to meet your performance, scale, and cost requirements.
With AWS Clean Rooms, you can configure Spark properties—such as shuffle partition settings for parallel processing and autoBroadcastJoinThreshold for optimizing join operations—to help you better control the behavior and tuning of SQL queries in a Clean Rooms collaboration. Additionally, you can choose to cache an existing table’s data containing results from a SQL query or create and cache a new table, which help improve the performance and reduce costs for complex queries using large datasets. For example, an advertiser running lift analysis on their advertising campaigns can specify a custom number of workers for an instance type and configure Spark properties—without editing their SQL query—to optimize costs.
With AWS Clean Rooms, customers can create a secure data clean room in minutes and collaborate with any company on AWS or Snowflake to generate unique insights about advertising campaigns, investment decisions, and research and development. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms.
Categories: marketing:marchitecture/analytics,general:products/aws-clean-rooms
Source: Amazon Web Services
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