AWS Glue

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. AWS Glue provides all the capabilities needed for data integration so that you can start analyzing your data and putting it to use in minutes instead of months.

Prerequisite Tasks

To use these operators, you must do a few things:

Generic Parameters

aws_conn_id

Reference to Amazon Web Services Connection ID. If this parameter is set to None then the default boto3 behaviour is used without a connection lookup. Otherwise use the credentials stored in the Connection. Default: aws_default

region_name

AWS Region Name. If this parameter is set to None or omitted then region_name from AWS Connection Extra Parameter will be used. Otherwise use the specified value instead of the connection value. Default: None

verify

Whether or not to verify SSL certificates.

  • False - Do not validate SSL certificates.

  • path/to/cert/bundle.pem - A filename of the CA cert bundle to use. You can specify this argument if you want to use a different CA cert bundle than the one used by botocore.

If this parameter is set to None or is omitted then verify from AWS Connection Extra Parameter will be used. Otherwise use the specified value instead of the connection value. Default: None

botocore_config

The provided dictionary is used to construct a botocore.config.Config. This configuration can be used to configure Avoid Throttling exceptions, timeouts, etc.

Example, for more detail about parameters please have a look botocore.config.Config
{
    "signature_version": "unsigned",
    "s3": {
        "us_east_1_regional_endpoint": True,
    },
    "retries": {
      "mode": "standard",
      "max_attempts": 10,
    },
    "connect_timeout": 300,
    "read_timeout": 300,
    "tcp_keepalive": True,
}

If this parameter is set to None or omitted then config_kwargs from AWS Connection Extra Parameter will be used. Otherwise use the specified value instead of the connection value. Default: None

Note

Specifying an empty dictionary, {}, will overwrite the connection configuration for botocore.config.Config

Operators

Create an AWS Glue crawler

AWS Glue Crawlers allow you to easily extract data from various data sources. To create a new AWS Glue Crawler or run an existing one you can use GlueCrawlerOperator.

tests/system/amazon/aws/example_glue.py

crawl_s3 = GlueCrawlerOperator(
    task_id="crawl_s3",
    config=glue_crawler_config,
)

Note

The AWS IAM role included in the config needs access to the source data location (e.g. s3:PutObject access if data is stored in Amazon S3) as well as the AWSGlueServiceRole policy. See the References section below for a link to more details.

Submit an AWS Glue job

To submit a new AWS Glue job you can use GlueJobOperator.

tests/system/amazon/aws/example_glue.py

submit_glue_job = GlueJobOperator(
    task_id="submit_glue_job",
    job_name=glue_job_name,
    script_location=f"s3://{bucket_name}/etl_script.py",
    s3_bucket=bucket_name,
    iam_role_name=role_name,
    create_job_kwargs={"GlueVersion": "3.0", "NumberOfWorkers": 2, "WorkerType": "G.1X"},
)

Note

The same AWS IAM role used for the crawler can be used here as well, but it will need policies to provide access to the output location for result data.

Create an AWS Glue Data Quality

AWS Glue Data Quality allows you to measure and monitor the quality of your data so that you can make good business decisions. To create a new AWS Glue Data Quality ruleset or update an existing one you can use GlueDataQualityOperator.

tests/system/amazon/aws/example_glue_data_quality.py

create_rule_set = GlueDataQualityOperator(
    task_id="create_rule_set",
    name=rule_set_name,
    ruleset=RULE_SET,
    data_quality_ruleset_kwargs={
        "TargetTable": {
            "TableName": athena_table,
            "DatabaseName": athena_database,
        }
    },
)

Start a AWS Glue Data Quality Evaluation Run

To start a AWS Glue Data Quality ruleset evaluation run you can use GlueDataQualityRuleSetEvaluationRunOperator.

tests/system/amazon/aws/example_glue_data_quality.py

start_evaluation_run = GlueDataQualityRuleSetEvaluationRunOperator(
    task_id="start_evaluation_run",
    datasource={
        "GlueTable": {
            "TableName": athena_table,
            "DatabaseName": athena_database,
        }
    },
    role=test_context[ROLE_ARN_KEY],
    rule_set_names=[rule_set_name],
)

Start a AWS Glue Data Quality Recommendation Run

To start a AWS Glue Data Quality rule recommendation run you can use GlueDataQualityRuleRecommendationRunOperator.

tests/system/amazon/aws/example_glue_data_quality_with_recommendation.py

recommendation_run = GlueDataQualityRuleRecommendationRunOperator(
    task_id="recommendation_run",
    datasource={
        "GlueTable": {
            "TableName": athena_table,
            "DatabaseName": athena_database,
        }
    },
    role=test_context[ROLE_ARN_KEY],
    recommendation_run_kwargs={"CreatedRulesetName": rule_set_name},
)

Sensors

Wait on an AWS Glue crawler state

To wait on the state of an AWS Glue crawler execution until it reaches a terminal state you can use GlueCrawlerSensor.

tests/system/amazon/aws/example_glue.py

wait_for_crawl = GlueCrawlerSensor(
    task_id="wait_for_crawl",
    crawler_name=glue_crawler_name,
)

Wait on an AWS Glue job state

To wait on the state of an AWS Glue Job until it reaches a terminal state you can use GlueJobSensor

tests/system/amazon/aws/example_glue.py

wait_for_job = GlueJobSensor(
    task_id="wait_for_job",
    job_name=glue_job_name,
    # Job ID extracted from previous Glue Job Operator task
    run_id=submit_glue_job.output,
    verbose=True,  # prints glue job logs in airflow logs
)

Wait on an AWS Glue Data Quality Evaluation Run

To wait on the state of an AWS Glue Data Quality RuleSet Evaluation Run until it reaches a terminal state you can use GlueDataQualityRuleSetEvaluationRunSensor

tests/system/amazon/aws/example_glue_data_quality.py

await_evaluation_run_sensor = GlueDataQualityRuleSetEvaluationRunSensor(
    task_id="await_evaluation_run_sensor",
    evaluation_run_id=start_evaluation_run.output,
)

Wait on an AWS Glue Data Quality Recommendation Run

To wait on the state of an AWS Glue Data Quality recommendation run until it reaches a terminal state you can use GlueDataQualityRuleRecommendationRunSensor

tests/system/amazon/aws/example_glue_data_quality_with_recommendation.py

await_recommendation_run_sensor = GlueDataQualityRuleRecommendationRunSensor(
    task_id="await_recommendation_run_sensor",
    recommendation_run_id=recommendation_run.output,
)

Wait on an AWS Glue Catalog Partition

To wait for a partition to show up in AWS Glue Catalog until it reaches a terminal state you can use GlueCatalogPartitionSensor

tests/system/amazon/aws/example_glue.py

wait_for_catalog_partition = GlueCatalogPartitionSensor(
    task_id="wait_for_catalog_partition",
    table_name="input",
    database_name=glue_db_name,
    expression="category='mixed'",
)

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