AWS Tools for Windows PowerShell
Command Reference

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Synopsis

Calls the Amazon Machine Learning CreateDataSourceFromRedshift API operation.

Syntax

New-MLDataSourceFromRedshift
-DatabaseInformation_ClusterIdentifier <String>
-ComputeStatistic <Boolean>
-DatabaseInformation_DatabaseName <String>
-DataSpec_DataRearrangement <String>
-DataSpec_DataSchema <String>
-DataSpec_DataSchemaUri <String>
-DataSourceId <String>
-DataSourceName <String>
-DatabaseCredentials_Password <String>
-RoleARN <String>
-DataSpec_S3StagingLocation <String>
-DataSpec_SelectSqlQuery <String>
-DatabaseCredentials_Username <String>
-Select <String>
-Force <SwitchParameter>
-ClientConfig <AmazonMachineLearningConfig>

Description

Creates a DataSource from a database hosted on an Amazon Redshift cluster. A DataSource references data that can be used to perform either CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations. CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING states can be used to perform only CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations. If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response. The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to transfer the result set of the SelectSqlQuery query to S3StagingLocation. After the DataSource has been created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also requires a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions. You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing datasource and copy the values to a CreateDataSource call. Change the settings that you want to change and make sure that all required fields have the appropriate values.

Parameters

-ClientConfig <AmazonMachineLearningConfig>
Amazon.PowerShell.Cmdlets.ML.AmazonMachineLearningClientCmdlet.ClientConfig
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-ComputeStatistic <Boolean>
The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesComputeStatistics
-DatabaseCredentials_Password <String>
The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesDataSpec_DatabaseCredentials_Password
-DatabaseCredentials_Username <String>
The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesDataSpec_DatabaseCredentials_Username
-DatabaseInformation_ClusterIdentifier <String>
The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesDataSpec_DatabaseInformation_ClusterIdentifier
-DatabaseInformation_DatabaseName <String>
The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesDataSpec_DatabaseInformation_DatabaseName
-DataSourceId <String>
A user-supplied ID that uniquely identifies the DataSource.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
-DataSourceName <String>
A user-supplied name or description of the DataSource.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-DataSpec_DataRearrangement <String>
A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.There are multiple parameters that control what data is used to create a datasource:
  • percentBeginUse percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.
  • percentEndUse percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.
  • complementThe complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
  • strategyTo change how Amazon ML splits the data for a datasource, use the strategy parameter.The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-DataSpec_DataSchema <String>
A JSON string that represents the schema for an Amazon Redshift DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource.A DataSchema is not required if you specify a DataSchemaUri.Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema.{ "version": "1.0","recordAnnotationFieldName": "F1","recordWeightFieldName": "F2","targetFieldName": "F3","dataFormat": "CSV","dataFileContainsHeader": true,"attributes": [{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],"excludedVariableNames": [ "F6" ] }
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-DataSpec_DataSchemaUri <String>
Describes the schema location for an Amazon Redshift DataSource.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-DataSpec_S3StagingLocation <String>
Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
-DataSpec_SelectSqlQuery <String>
Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
-Force <SwitchParameter>
This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-RoleARN <String>
A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:
  • A security group to allow Amazon ML to execute the SelectSqlQuery query on an Amazon Redshift cluster
  • An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the S3StagingLocation
Required?True
Position?Named
Accept pipeline input?True (ByPropertyName)
-Select <String>
Use the -Select parameter to control the cmdlet output. The default value is 'DataSourceId'. Specifying -Select '*' will result in the cmdlet returning the whole service response (Amazon.MachineLearning.Model.CreateDataSourceFromRedshiftResponse). Specifying the name of a property of type Amazon.MachineLearning.Model.CreateDataSourceFromRedshiftResponse will result in that property being returned. Specifying -Select '^ParameterName' will result in the cmdlet returning the selected cmdlet parameter value.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)

Common Credential and Region Parameters

-AccessKey <String>
The AWS access key for the user account. This can be a temporary access key if the corresponding session token is supplied to the -SessionToken parameter.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesAK
-Credential <AWSCredentials>
An AWSCredentials object instance containing access and secret key information, and optionally a token for session-based credentials.
Required?False
Position?Named
Accept pipeline input?True (ByValue, ByPropertyName)
-EndpointUrl <String>
The endpoint to make the call against.Note: This parameter is primarily for internal AWS use and is not required/should not be specified for normal usage. The cmdlets normally determine which endpoint to call based on the region specified to the -Region parameter or set as default in the shell (via Set-DefaultAWSRegion). Only specify this parameter if you must direct the call to a specific custom endpoint.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
-NetworkCredential <PSCredential>
Used with SAML-based authentication when ProfileName references a SAML role profile. Contains the network credentials to be supplied during authentication with the configured identity provider's endpoint. This parameter is not required if the user's default network identity can or should be used during authentication.
Required?False
Position?Named
Accept pipeline input?True (ByValue, ByPropertyName)
-ProfileLocation <String>
Used to specify the name and location of the ini-format credential file (shared with the AWS CLI and other AWS SDKs)If this optional parameter is omitted this cmdlet will search the encrypted credential file used by the AWS SDK for .NET and AWS Toolkit for Visual Studio first. If the profile is not found then the cmdlet will search in the ini-format credential file at the default location: (user's home directory)\.aws\credentials.If this parameter is specified then this cmdlet will only search the ini-format credential file at the location given.As the current folder can vary in a shell or during script execution it is advised that you use specify a fully qualified path instead of a relative path.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesAWSProfilesLocation, ProfilesLocation
-ProfileName <String>
The user-defined name of an AWS credentials or SAML-based role profile containing credential information. The profile is expected to be found in the secure credential file shared with the AWS SDK for .NET and AWS Toolkit for Visual Studio. You can also specify the name of a profile stored in the .ini-format credential file used with the AWS CLI and other AWS SDKs.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesStoredCredentials, AWSProfileName
-Region <Object>
The system name of an AWS region or an AWSRegion instance. This governs the endpoint that will be used when calling service operations. Note that the AWS resources referenced in a call are usually region-specific.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesRegionToCall
-SecretKey <String>
The AWS secret key for the user account. This can be a temporary secret key if the corresponding session token is supplied to the -SessionToken parameter.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesSK, SecretAccessKey
-SessionToken <String>
The session token if the access and secret keys are temporary session-based credentials.
Required?False
Position?Named
Accept pipeline input?True (ByPropertyName)
AliasesST

Outputs

System.String or Amazon.MachineLearning.Model.CreateDataSourceFromRedshiftResponse
This cmdlet returns a System.String object. The service call response (type Amazon.MachineLearning.Model.CreateDataSourceFromRedshiftResponse) can also be referenced from properties attached to the cmdlet entry in the $AWSHistory stack.
Developer Guide
Machine Learning Concepts
Service API Reference
AWS Tools for PowerShell User Guide

Supported Version

AWS Tools for PowerShell: 2.x.y.z