How do you automate pyspark jobs on emr using boto3 (or otherwise)?

Just do this using AWS Data Pipeline. You can setup your S3 bucket to trigger a lambda function every time a new file is placed inside the bucket https://docs.aws.amazon.com/lambda/latest/dg/with-s3-example.html. Then your Lambda function will activate your Data Pipeline https://aws.amazon.com/blogs/big-data/using-aws-lambda-for-event-driven-data-processing-pipelines/ then your Data Pipeline spins up a new EMR Cluster using EmrCluster, then you can specify your bootstrap options, then you can run your EMR commands using EmrActivity, and when it's all done it'll terminate your EMR Cluster and deactivate the Data Pipeline.

Take a look at boto3 EMR docs to create the cluster. You essentially have to call run_job_flow and create steps that runs the program you want.

import boto3    

client = boto3.client('emr', region_name='us-east-1')

S3_BUCKET = 'MyS3Bucket'
S3_KEY = 'spark/main.py'
S3_URI = 's3://{bucket}/{key}'.format(bucket=S3_BUCKET, key=S3_KEY)

# upload file to an S3 bucket
s3 = boto3.resource('s3')
s3.meta.client.upload_file("myfile.py", S3_BUCKET, S3_KEY)

response = client.run_job_flow(
    Name="My Spark Cluster",
    ReleaseLabel='emr-4.6.0',
    Instances={
        'MasterInstanceType': 'm4.xlarge',
        'SlaveInstanceType': 'm4.xlarge',
        'InstanceCount': 4,
        'KeepJobFlowAliveWhenNoSteps': True,
        'TerminationProtected': False,
    },
    Applications=[
        {
            'Name': 'Spark'
        }
    ],
    BootstrapActions=[
        {
            'Name': 'Maximize Spark Default Config',
            'ScriptBootstrapAction': {
                'Path': 's3://support.elasticmapreduce/spark/maximize-spark-default-config',
            }
        },
    ],
    Steps=[
    {
        'Name': 'Setup Debugging',
        'ActionOnFailure': 'TERMINATE_CLUSTER',
        'HadoopJarStep': {
            'Jar': 'command-runner.jar',
            'Args': ['state-pusher-script']
        }
    },
    {
        'Name': 'setup - copy files',
        'ActionOnFailure': 'CANCEL_AND_WAIT',
        'HadoopJarStep': {
            'Jar': 'command-runner.jar',
            'Args': ['aws', 's3', 'cp', S3_URI, '/home/hadoop/']
        }
    },
    {
        'Name': 'Run Spark',
        'ActionOnFailure': 'CANCEL_AND_WAIT',
        'HadoopJarStep': {
            'Jar': 'command-runner.jar',
            'Args': ['spark-submit', '/home/hadoop/main.py']
        }
    }
    ],
    VisibleToAllUsers=True,
    JobFlowRole='EMR_EC2_DefaultRole',
    ServiceRole='EMR_DefaultRole'
)

You can also add steps to a running cluster if you know the job flow id:

job_flow_id = response['JobFlowId']
print("Job flow ID:", job_flow_id)

step_response = client.add_job_flow_steps(JobFlowId=job_flow_id, Steps=SomeMoreSteps)

step_ids = step_response['StepIds']

print("Step IDs:", step_ids)

For more configurations, check out sparksteps.

Actually, I've gone with AWS's Step Functions, which is a state machine wrapper for Lambda functions, so you can use boto3 to start the EMR Spark job using run_job_flow and you can use describe_cluaster to get the status of the cluster. Finally use a choice. SO your step functions look something like this (step function types in brackets:

Run job (task) -> Wait for X min (wait) -> Check status (task) -> Branch (choice) [ => back to wait, or => done ]