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fs.s3.cse.encryptionMaterialsProvider N/A The EncryptionMaterialsProvider class path used with client- side encryption. When using CSE-KMS, specify com.amazon.ws.emr.hadoop.fs.cse.KMSEnc 85
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Amazon EMR Management Guide Control Cluster Termination Property Default value Description fs.s3.cse.materialsDescription.enabled false When set to true , populates the materialsDescription of encrypted objects with the Amazon S3 URI for the object and the JobFlowId. Set to true when using custom encryption materials. fs.s3.cse.kms.keyId N/A Applies when using CSE-KMS. The value of the KeyId, ARN, or alias of the AWS KMS CMK used for encryption. fs.s3.cse.cryptoStorageMode ObjectMetadata The Amazon S3 storage mode. By default, the description of the encryption information is stored in the object metadata. You can also store the description in an instruction file. Valid values are ObjectMetadata and InstructionFile. For more information, see Client-Side Data Encryption with the AWS SDK for Java and Amazon S3 . Control Cluster Termination When you create a cluster using Amazon EMR, you can choose to create a transient cluster that auto- terminates after steps complete, or you can create a long-running cluster that continues to run until you terminate it deliberately. When a cluster terminates, all Amazon EC2 instances in the cluster terminate, and data in the instance store and EBS volumes is no longer available and not recoverable. Understanding and managing cluster termination is critical to developing a strategy to manage and preserve data by writing to Amazon S3 and balancing cost. For information about how to terminate a cluster manually, see Terminate a Cluster (p. 326) . When you use auto-termination, the cluster starts, runs any bootstrap actions that you specify, and then executes steps that typically input data, process the data, and then produce and save output. When the steps finish, Amazon EMR automatically terminates the cluster Amazon EC2 instances. This is an effective model for a cluster that performs a periodic processing task, such as a daily data processing run. Auto- terminating a cluster helps ensure that you are billed only for the time required to process your data. For more information about steps, see Work with Steps Using the AWS CLI and Console (p. 345) . With a long-running cluster, the cluster starts the same way. You can specify steps as you would with a cluster that terminates automatically, but the cluster continues to run and accrue charges after steps complete. This model is effective when you need to interactively or automatically query data, or interact with big data applications hosted on the cluster on an ongoing basis. It is also effective if you periodically process a data set so large or so frequently that it is inefficient to launch new clusters and load data each time. You can enable termination protection on long-running clusters to help prevent accidental shutdown. You can also take advantage of features like automatic scaling and instance fleets to dynamically size the cluster to balance performance and cost in response to workload demands. For
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  • Spring '12
  • LauraParker
  • Amazon Web Services, Amazon Elastic Compute Cloud

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