1. Coordinating Data Pipeline Executions with AWS Scheduler


    When you want to coordinate data pipeline executions, you can use AWS services like AWS Step Functions for coordinating multi-step workflows, or AWS Glue for data integration tasks. Both offer scheduling capabilities. However, if you specifically need to schedule AWS resources, you can use Amazon EventBridge (formerly CloudWatch Events) to trigger your pipelines on a schedule.

    AWS EventBridge allows you to create a rule that will run on a schedule you define using either a cron expression or rate expression. You can then configure this rule to trigger any AWS service that is integrated with EventBridge, such as starting an AWS Glue job, an AWS Batch job, or a Lambda function that can initiate your data pipeline.

    Below, I'll provide you with a Pulumi program that creates an EventBridge rule to trigger an AWS Glue job on a schedule. This AWS Glue job could be the start of your data pipeline. We define the schedule using a cron expression, and we assume that an AWS Glue job named "MyDataPipelineJob" is already defined in your AWS account.

    The following Pulumi program is written in Python, which is a popular language for infrastructure as code with Pulumi. Make sure you have Pulumi installed, have an AWS account, and have configured your AWS credentials for use with Pulumi.

    import pulumi import pulumi_aws as aws # Define an AWS Glue job (you should replace "my_data_pipeline_job_name" with your actual AWS Glue job name) # This part is commented out because it's assumed you already have a Glue job created outside of this Pulumi program. # glue_job = aws.glue.Job("MyDataPipelineJob", # name="my_data_pipeline_job_name", # role_arn="arn:aws:iam::ACCOUNT_ID:role/service-role/AWSGlueServiceRole-MyGlueJob", # command=aws.glue.JobCommandArgs( # script_location="s3://my-glue-scripts/my-data-pipeline-job.py", # python_version="3" # ) # ) # Create an EventBridge Rule to trigger the Glue job on a schedule. # The schedule uses a cron expression to run at 8am UTC every day. schedule_rule = aws.cloudwatch.EventRule("MyDataPipelineScheduler", schedule_expression="cron(0 8 * * ? *)", # This will run every day at 8:00 AM UTC. description="Scheduler Rule for My Data Pipeline Glue Job" ) # Define the target for the EventBridge rule, which is our AWS Glue job. # The "arn" should be the ARN of your existing AWS Glue job. event_target = aws.cloudwatch.EventTarget("MyGlueJobEventTarget", rule=schedule_rule.name, arn="arn:aws:glue:REGION:ACCOUNT_ID:job/my_data_pipeline_job_name", # Replace with your Glue job's ARN. role_arn="arn:aws:iam::ACCOUNT_ID:role/service-role/Amazon_EventBridge_Invoke_Glue_Job_Role" # IAM role with permissions to invoke Glue jobs. ) # Export the names of the resources we created pulumi.export("schedule_rule_name", schedule_rule.name) pulumi.export("event_target_id", event_target.id)

    In this program, we:

    • Commented out the actual creation of an AWS Glue job, assuming you already have one.
    • Defined an EventBridge rule called MyDataPipelineScheduler that invokes the target based on a cron schedule.
    • Added an event target that ties the rule to a specific AWS Glue job ARN.
    • The role_arn should reference an IAM role that grants sufficient permissions to EventBridge to trigger your AWS Glue jobs.
    • Added some Pulumi exports to give us the name of the schedule and target ID after deployment, which can aid in debugging or manual review.

    Please replace placeholder values like REGION, ACCOUNT_ID, and my_data_pipeline_job_name with your actual AWS region, account ID, and the name of the Glue job you want to schedule, respectively.

    The cron expression cron(0 8 * * ? *) is set to trigger the execution every day at 8 AM UTC. You can adjust it to match your scheduling needs. The cron fields are Minutes Hours Day-of-month Month Day-of-week Year.

    To run this code, save it to a file __main__.py, and run pulumi up in the same directory. You will see Pulumi perform a preview, and after you confirm, it will provision the resources in your AWS account.