Then the producer account administrator authorizes sharing data for the specified consumer. First, a producer cluster administrator creates a datashare, adds objects, and gives access to the consumer account. You just pay for the Amazon Redshift clusters that participate in sharing.Ĭross-account data sharing is a two-step process. Pricing – Cross-account data sharing is available across clusters that are in the same Region.California), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), Europe (Stockholm), and South America (São Paulo). Regions – Cross-account data sharing is available for all Amazon Redshift RA3 node types in US East (N.Encryption – For data sharing to work, both the producer and consumer clusters must be encrypted and should be in the same AWS Region.When instantiating an Amazon Redshift cluster, make sure to choose the RA3 cluster type. Cluster type – Data sharing is supported in the RA3 cluster type.Multiple AWS accounts – You need at least two AWS accounts: a producer account and a consumer account.Analyze and process data with Data Wrangler in the consumer account and build your data preparation workflows.īe aware of the considerations for working with Amazon Redshift data sharing:.Access the Amazon Redshift datashare in the consumer account.Create an Amazon Redshift datashare in the producer account and allow the consumer account to access the data.Instantiate an Amazon Redshift RA3 cluster in the producer account and load the dataset.The following is a high-level overview of the workflow: To follow along, download the dataset to your local machine. For this post, we use the banking dataset. We start with two AWS accounts: a producer account with the Amazon Redshift data warehouse, and a consumer account for SageMaker ML use cases. In this post, we walk through setting up a cross-account integration using an Amazon Redshift datashare and preparing data using Data Wrangler. Data Wrangler allows you to explore and transform data for ML by connecting to Amazon Redshift datashares. Amazon SageMaker Data Wrangler is a capability of Amazon SageMaker that makes it faster for data scientists and engineers to prepare data for ML applications by using a visual interface. The Amazon Redshift cross-account data sharing feature provides a simple and secure way to share fresh, complete, and consistent data in your Amazon Redshift data warehouse with any number of stakeholders in different AWS accounts. Manually building and maintaining the different extract, transform, and load (ETL) jobs in different accounts adds complexity and cost, and makes it more difficult to maintain the governance, compliance, and security best practices to keep your data safe.Īmazon Redshift is a fast, fully managed cloud data warehouse. Organizations with a multi-account architecture want to avoid situations where they must extract data from one account and load it into another for data preparation activities. To make ML-based decisions from data, you need your data available, accessible, clean, and in the right format to train ML models. Organizations moving towards a data-driven culture embrace the use of data and machine learning (ML) in decision-making.
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