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@noahgift
Created February 13, 2023 18:09
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# Amazon Forecast Example
[source, python]
----
import boto3
forecast = boto3.client('forecast')
response = forecast.create_dataset_group(
DatasetGroupName='forecast_dataset_group',
Domain='CUSTOM',
)
----
# Amazon QuickSight Example
[source, python]
----
import boto3
quicksight = boto3.client('quicksight')
response = quicksight.create_data_source(
AwsAccountId='AWS_ACCOUNT_ID',
DataSourceId='data_source_id',
Name='data_source_name',
Type='ADOBE_ANALYTICS',
)
----
# Amazon Kinesis Data Streams Example
[source, python]
----
import boto3
kinesis = boto3.client('kinesis')
response = kinesis.create_stream(
StreamName='kinesis_stream_name',
ShardCount=1,
)
----
# Amazon Kinesis Data Analytics Example
[source, python]
----
import boto3
kinesis_analytics = boto3.client('kinesisanalytics')
response = kinesis_analytics.create_application(
ApplicationName='kinesis_analytics_app',
RuntimeEnvironment='SQL-1.0',
)
----
# Amazon SageMaker Example
[source, python]
----
import boto3
sagemaker = boto3.client('sagemaker')
response = sagemaker.create_notebook_instance(
NotebookInstanceName='notebook_instance_name',
InstanceType='ml.t2.medium',
)
----
# Amazon DynamoDB Example
[source, python]
----
import boto3
dynamodb = boto3.client('dynamodb')
response = dynamodb.create_table(
TableName='dynamodb_table_name',
KeySchema=[
{
'AttributeName': 'attribute_name',
'KeyType': 'HASH'
},
],
AttributeDefinitions=[
{
'AttributeName': 'attribute_name',
'AttributeType': 'S'
},
],
ProvisionedThroughput={
'ReadCapacityUnits': 5,
'WriteCapacityUnits': 5
}
)
----
# Amazon EMR Example
[source, python]
----
import boto3
emr = boto3.client('emr')
response = emr.run_job_flow(
Name='emr_cluster_name',
Instances={
'InstanceGroups': [
{
'Name': 'master_instance_group',
'InstanceRole': 'MASTER',
'InstanceType': 'm5.xlarge',
'InstanceCount': 1,
},
],
'Ec2KeyName': 'ec2_key_name',
'KeepJobFlowAlive
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