How to use the command 'aws glue' (with examples)

AWS Glue is a fully managed extract, transform, and load (ETL) service provided by Amazon Web Services (AWS). Its primary goal is to help businesses efficiently prepare and transform their data for analytics. By using the AWS Glue command-line interface (CLI), you can automate and manage your data workflows in the cloud with ease. Whether you are looking to list available jobs, initiate a job run, or manage dev endpoints, the following examples demonstrate how to leverage the aws glue CLI command effectively.

Use case 1: List jobs

Code:

aws glue list-jobs

Motivation:

Listing jobs in AWS Glue is a fundamental operation that allows you to view all the jobs defined in your AWS account. This is particularly useful when you need an overview of the current ETL jobs to manage, update, or remove outdated processes. By utilizing this command, data engineers can ensure that they have a full inventory of all active transformations, boosting the organization’s data pipeline efficiency.

Explanation:

• aws glue: This initiates the execution of a command within the AWS Glue CLI environment.
• list-jobs: As the sub-command, it queries the AWS Glue service to retrieve a list of all registered jobs.

Example output:

{
    "JobNames": [
        "job1",
        "data-transformation-job",
        "sales-etl-job"
    ]
}

Use case 2: Start a job

Code:

aws glue start-job-run --job-name job_name

Motivation:

Starting a job run in AWS Glue is essential when you want to execute a specific ETL process. This command transforms input data based on defined scripts and stores the output in a specified data repository. It comes in handy when you’ve updated an ETL script and need to process data promptly, enabling timely data analysis and insights.

Explanation:

• aws glue: Calls the AWS Glue CLI.
• start-job-run: This sub-command requests the execution of a job.
• --job-name job_name: The --job-name flag specifies the identifier of the job you wish to initiate. Replace job_name with the exact name of the job you want to run.

Example output:

{
    "JobRunId": "jr_1234567890abcdef"
}

Use case 3: Start running a workflow

Code:

aws glue start-workflow-run --name workflow_name

Motivation:

Starting a workflow run is crucial when your data processing is arranged in multiple related jobs encapsulated in a workflow. This command launches an entire workflow, which may include numerous data transformations, thereby supporting complex data processing pipelines essential for large-scale data applications.

Explanation:

• aws glue: Executes a request in the AWS Glue CLI.
• start-workflow-run: Initiates the full execution of a workflow.
• --name workflow_name: The --name flag denotes the specific workflow you want to run. Replace workflow_name with the name of your workflow.

Example output:

{
    "RunId": "wr_0987654321abcdef"
}

Use case 4: List triggers

Code:

aws glue list-triggers

Motivation:

Triggers in AWS Glue automate the execution of jobs or workflows based on specified conditions. Listing triggers helps administrators oversee the existing automation rules, ensuring that ETL tasks are commenced as expected, reducing the need for manual intervention and promoting a streamlined data processing cycle.

Explanation:

• aws glue: Activates a command under the AWS Glue CLI context.
• list-triggers: Requests a listing of all triggers set up within your Glue service.

Example output:

{
    "TriggerNames": [
        "trigger1",
        "daily-processing-trigger",
        "hourly-cleanup-trigger"
    ]
}

Use case 5: Start a trigger

Code:

aws glue start-trigger --name trigger_name

Motivation:

Starting a trigger is beneficial when you want to manually activate a sequence of jobs or workflows previously defined. This can be particularly helpful in testing or debugging scenarios, when you need to confirm the behavior of the workflow, or in circumstances where automatic conditions are not met.

Explanation:

• aws glue: Accesses the AWS Glue command interface.
• start-trigger: Directly initiates a defined trigger.
• --name trigger_name: Identifies the trigger to be started, where trigger_name should be the exact name configured in AWS Glue.

Example output:

{
    "Name": "trigger_name"
}

Use case 6: Create a dev endpoint

Code:

aws glue create-dev-endpoint --endpoint-name name --role-arn role_arn_used_by_endpoint

Motivation:

Development endpoints in AWS Glue are essential for testing and developing ETL scripts before deploying them to production. By creating a dev endpoint, developers can experiment with various data transformations using interactive sessions. This ensures operational scripts are fully vetted, minimizing errors during production runs.

Explanation:

• aws glue: Begins a session with the AWS Glue command line tool.
• create-dev-endpoint: Initializes a development endpoint for interactive data script development.
• --endpoint-name name: Specifies a unique identifier for the development endpoint. Replace name with your desired endpoint designation.
• --role-arn role_arn_used_by_endpoint: This parameter defines the AWS Identity and Access Management (IAM) role to be assumed by the endpoint. Replace role_arn_used_by_endpoint with the actual ARN (Amazon Resource Name) of the role allocated for this purpose.

Example output:

{
    "EndpointName": "name",
    "Status": "CREATING"
}

Conclusion:

By utilizing these use cases and examples, users can efficiently handle a wide variety of tasks within AWS Glue. From listing available jobs and triggers to starting job runs and workflow executions, AWS Glue’s CLI offers powerful tools for organizations to automate and streamline their data processing pipelines legitimately and effectively. Each command, by nature, empowers businesses to maintain robust ETL operations, thereby leading to more informed decision-making and a smoother data-driven approach.