# 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
Docstring:
::
%sm_analytics [--auth-type AUTH_TYPE] [--cluster-id CLUSTER_ID]
[--language LANGUAGE]
[--assumable-role-arn ASSUMABLE_ROLE_ARN]
[--emr-execution-role-arn EMR_EXECUTION_ROLE_ARN]
[--secret SECRET]
[--verify-certificate VERIFY_CERTIFICATE]
[--no-override-krb5-conf OPTIONAL]
[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 {'Basic_Access', 'Kerberos',
'None'}.
--cluster-id CLUSTER_ID
The cluster id to connect to.
--language LANGUAGE Language to use. The supported languages for IPython
kernel(s) are {'python', 'scala'}. This is a required
argument for IPython kernels, but not for magic
kernels such as PySpark or SparkScala.
--assumable-role-arn ASSUMABLE_ROLE_ARN
The IAM role to assume when connecting to a cluster in
a different AWS account. This argument is not required
when connecting to a cluster in the same AWS account.
--emr-execution-role-arn EMR_EXECUTION_ROLE_ARN
The IAM role passed to EMR to set up EMR job security
context. This argument is optional and used when IAM
Passthrough feature is enabled for EMR.
--secret SECRET The AWS Secrets Manager SecretID.
--verify-certificate VERIFY_CERTIFICATE
Determine if SSL certificate should be verified when
using HTTPS to connect to EMR. Supported values are
['True', 'False', 'PathToCert']. If a path-to-cert-
file is provided, the certificate verification will be
done with the certificate in the provided file
path.Note that the default
--no-override-krb5-conf OPTIONAL
This argument is used standalone (no input value needed)
and only when connecting to Kerberos cluster. With this
key SageMaker will not override existing krb5.conf file.
User should make sure there is krb5.conf file at
/etc/krb5.conf. If this key this not present, Sagemaker
will generate and override the krb5.conf file by default.
```
### 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.
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"description": "# SageMaker Studio Analytics Extension\n\nThis 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.\n\n## Usage\nBefore 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. \n### Register the magic command:\n```buildoutcfg\n%load_ext sagemaker_studio_analytics_extension.magics\n```\n### Show help content:\n```buildoutcfg\nDocstring:\n::\n\n %sm_analytics [--auth-type AUTH_TYPE] [--cluster-id CLUSTER_ID]\n [--language LANGUAGE]\n [--assumable-role-arn ASSUMABLE_ROLE_ARN]\n [--emr-execution-role-arn EMR_EXECUTION_ROLE_ARN]\n [--secret SECRET]\n [--verify-certificate VERIFY_CERTIFICATE]\n [--no-override-krb5-conf OPTIONAL]\n [command [command ...]]\n\npositional arguments:\n command Command to execute. The command consists of a service\n name followed by a ' ' followed by an operation.\n Supported services are ['emr'] and supported\n operations are ['connect']. For example a valid\n command is 'emr connect'.\n\noptional arguments:\n --auth-type AUTH_TYPE\n The authentication type to be used. Supported\n authentication types are {'Basic_Access', 'Kerberos',\n 'None'}.\n --cluster-id CLUSTER_ID\n The cluster id to connect to.\n --language LANGUAGE Language to use. The supported languages for IPython\n kernel(s) are {'python', 'scala'}. This is a required\n argument for IPython kernels, but not for magic\n kernels such as PySpark or SparkScala.\n --assumable-role-arn ASSUMABLE_ROLE_ARN\n The IAM role to assume when connecting to a cluster in\n a different AWS account. This argument is not required\n when connecting to a cluster in the same AWS account.\n --emr-execution-role-arn EMR_EXECUTION_ROLE_ARN\n The IAM role passed to EMR to set up EMR job security\n context. This argument is optional and used when IAM\n Passthrough feature is enabled for EMR.\n --secret SECRET The AWS Secrets Manager SecretID.\n --verify-certificate VERIFY_CERTIFICATE\n Determine if SSL certificate should be verified when\n using HTTPS to connect to EMR. Supported values are\n ['True', 'False', 'PathToCert']. If a path-to-cert-\n file is provided, the certificate verification will be\n done with the certificate in the provided file\n path.Note that the default\n --no-override-krb5-conf OPTIONAL\n This argument is used standalone (no input value needed) \n and only when connecting to Kerberos cluster. With this \n key SageMaker will not override existing krb5.conf file.\n User should make sure there is krb5.conf file at \n /etc/krb5.conf. If this key this not present, Sagemaker\n will generate and override the krb5.conf file by default. \n```\n\n### Examples\n1. Connect Studio notebook using IPython Kernel to EMR cluster protected by Kerberos. \n```buildoutcfg\n%sm_analytics emr connect --cluster-id j-1JIIZS02SEVCS --auth-type Kerberos --language python\n```\n\n2. Connect Studio notebook using IPython Kernel to HTTP Basic Auth protected EMR cluster and create the Scala based session. \n```buildoutcfg\n%sm_analytics emr connect --cluster-id j-1KHIOQZAQUF5P --auth-type Basic_Access --language scala\n```\n\n3. Connect Studio notebook using IPython Kernel to EMR cluster directly without Livy authentication. \n```buildoutcfg\n%sm_analytics emr connect --cluster-id j-1KHIOQZAQUF5P --auth-type None --language python\n```\n\n4. Connect Studio notebook using PySpark or Spark(scala) Kernel to HTTP Basic Auth protected EMR cluster. \n```buildoutcfg\n%sm_analytics emr connect --cluster-id j-1KHIOQZAQUF5P --auth-type Basic_Access\n```\n## License\n\nThis library is licensed under the Apache 2.0 License. See the LICENSE file.\n\n",
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