Metadata-Version: 2.1
Name: sagemaker-studio-analytics-extension
Version: 0.0.1.5
Summary: SageMaker Studio Analytics Extension
Home-page: https://aws.amazon.com/sagemaker
Author: Amazon Web Services
License: Apache 2.0
Description: # SageMaker Studio Analytics Extension
        
        This is a notebook extension provided by AWS SageMaker Studio Team to integrate with analytics resources. Currently, it supports connecting SageMaker Studio Notebook to Spark(EMR) cluster through SparkMagic library.
        
        ## Usage
        Before you can use the magic command to connect Studio notebook to EMR, please ensure the SageMaker Studio has the connectivity to Spark cluster(livy service). You can refer to [this AWS blog](https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-studio-notebooks-backed-by-spark-in-amazon-emr/) for how to set up SageMaker Studio and EMR cluster. 
        ### Register the magic command:
        ```buildoutcfg
        %load_ext sagemaker_studio_analytics_extension.magics
        ```
        ### Show help content:
        ```buildoutcfg
        %sm_analytics?
        
        Docstring:
        ::
        
          %sm_analytics [--auth-type AUTH_TYPE] [--cluster-id CLUSTER_ID]
                            [--language LANGUAGE]
                            [command [command ...]]
        
        positional arguments:
          command               Command to execute. The command consists of a service
                                name followed by a ' ' followed by an operation.
                                Supported services are {'emr'} and supported
                                operations are {'connect'}. For example a valid
                                command is 'emr connect'.
        
        optional arguments:
          --auth-type AUTH_TYPE
                                The authentication type to be used. Supported
                                authentication types are {'Kerberos', 'None',
                                'Basic_Access'}.
          --cluster-id CLUSTER_ID
                                The cluster id to connect to.
          --language LANGUAGE   Language to use. The supported languages for IPython
                                kernel(s) are {'scala', 'python'}. This is a required
                                argument for IPython kernels, but not for magic
                                kernels such as PySpark or SparkScala.
        ```
        
        ### Examples
        1. Connect Studio notebook using IPython Kernel to EMR cluster protected by Kerberos. 
        ```buildoutcfg
        %sm_analytics emr connect --cluster-id j-1JIIZS02SEVCS --auth-type Kerberos --language python
        ```
        
        2. Connect Studio notebook using IPython Kernel to HTTP Basic Auth protected EMR cluster and create the Scala based session.  
        ```buildoutcfg
        %sm_analytics emr connect --cluster-id j-1KHIOQZAQUF5P --auth-type Basic_Access  --language scala
        ```
        
        3. Connect Studio notebook using IPython Kernel to EMR cluster directly without Livy authentication. 
        ```buildoutcfg
        %sm_analytics emr connect --cluster-id j-1KHIOQZAQUF5P --auth-type None  --language python
        ```
        
        4. Connect Studio notebook using PySpark or Spark(scala) Kernel to HTTP Basic Auth protected EMR cluster. 
        ```buildoutcfg
        %sm_analytics emr connect --cluster-id j-1KHIOQZAQUF5P --auth-type Basic_Access
        ```
        ## License
        
        This library is licensed under the Apache 2.0 License. See the LICENSE file.
        
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
