![]() The following screenshot shows sample SQL commands to enable data sharing on the Amazon Redshift provisioned producer cluster. Note that the Redshift Serverless endpoint is encrypted by default the provisioned Redshift producer cluster also needs to be encrypted for data sharing to work between them. ![]() Grant usage on this data share to the Redshift Serverless consumer namespace, using the Redshift Serverless endpoint’s namespace ID.Add objects you want to share to the data share.Enable data sharing between the Amazon Redshift provisioned cluster (producer) and the data science Redshift Serverless endpoint (consumer) using these high-level steps:.The data science team can create a new Redshift Serverless endpoint, as described in the previous use case.The following steps need to be performed to implement this architecture: The following diagram illustrates the new architecture. To address these issues, they decide to let the data science team create their own new Redshift Serverless instance and grant them data share access to the data they need from the existing Amazon Redshift provisioned cluster. A chargeback or cost allocation model is desired for the various teams consuming data.Because the current cluster resources are optimally utilized, they need to ensure workload isolation to support the needs of the new team without impacting existing workloads.The additional compute capacity needs of the new team are unknown and hard to estimate.Their data analysts, data scientists, and business analysts can start querying and analyzing the data with ease and derive business insights quickly without worrying about infrastructure, tuning, and administrative tasks.Īdding the new data science group to the current cluster presented the following challenges: They can create a new Redshift Serverless endpoint in a few minutes and load their initial few TBs of marketing dataset into Redshift Serverless quickly. In this case, they can use Redshift Serverless to satisfy their needs. Given their limited resources, they want minimal infrastructure and administrative overhead. They want to create new marketing analytics quickly and easily, to determine the ROI and effectiveness of their marketing efforts. The customer doesn’t have any IT administrators, and their staff is comprised of data analysts, a data scientist, and business analysts. In our first use case, a startup company with limited resources needs to create a new data warehouse and reports for marketing analytics. Cost-optimization of sporadic workloads – An existing customer is looking to optimize the cost of their Amazon Redshift producer cluster with sporadic batch ingestion workloads.Optimize workload performance – An existing Amazon Redshift customer is looking to optimize the performance of their variable reporting workloads during peak time.A new team needs quick self-service access to the Amazon Redshift data to create forecasting and predictive models for the business. Self-service analytics – An existing Amazon Redshift customer has a provisioned Amazon Redshift cluster that is right-sized for their current workload.They have very limited IT resources, and need to get started quickly and easily with minimal infrastructure or administrative overhead. Easy analytics – A startup company needs to create a new data warehouse and reports for marketing analytics.In this post, we discuss four different use cases of Redshift Serverless: ![]()
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