1. AI Data Lineage and Metadata Management with AWS Glue Crawler


    AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. Within AWS Glue, a service called Glue Crawler can be used to discover data sources, identify data formats, and suggest schemas and transformations to turn raw data into actionable data sets.

    The Glue Crawler can scan various data stores such as Amazon S3, DynamoDB tables, or relational databases via JDBC, and automatically infer schemas and create metadata tables in the Glue Data Catalog. This catalog serves as a central metadata repository that is integrated with Amazon Athena, Amazon Redshift Spectrum, and Amazon EMR, providing a unified view of your data across these services.

    In Pulumi, we can define our AWS Glue Crawler resources utilizing the AWS SDK for Pulumi. Below is a program that creates an AWS Glue Crawler for scanning an S3 data store, sets up a schedule to run the crawler, and integrates it with a Glue Data Catalog database.

    Before we start, make sure you've configured your AWS provider within Pulumi as follows:

    pulumi config set aws:region <your-aws-region>

    Replace <your-aws-region> with the AWS Region where you want to create the Glue Crawler.

    Now, let's look at the Pulumi program to deploy an AWS Glue Crawler for efficient data lineage and metadata management.

    import pulumi import pulumi_aws as aws # Create an IAM role for AWS Glue Crawler. glue_crawler_role = aws.iam.Role("glue-crawler-role", assume_role_policy={ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "glue.amazonaws.com", }, }], } ) # Attach the AWS managed policy for AWS Glue Service to the role. glue_service_policy_attachement = aws.iam.RolePolicyAttachment("glue-service-policy-attachment", role=glue_crawler_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSGlueServiceRole" ) # Create an S3 bucket where data is stored and which the crawler will scan. data_bucket = aws.s3.Bucket("data-bucket") # Define the Glue Crawler to scan an S3 data store. glue_crawler = aws.glue.Crawler("data-crawler", role=glue_crawler_role.arn, database_name="my_database", s3_targets=[{ "path": data_bucket.bucket.apply(lambda name: "s3://" + name) # Use the bucket name to form the S3 path }], schedule="cron(0 2 * * ? *)", # Format is cron(minute hour day-of-month month day-of-week year) ) # Output the name of the bucket and the ARN of the Glue Crawler. pulumi.export('dataBucketName', data_bucket.bucket) pulumi.export('glueCrawlerArn', glue_crawler.arn)

    In the above program:

    • We create an IAM role named glue-crawler-role with an attached AWS managed policy AWSGlueServiceRole. This role is used by the AWS Glue Crawler to access other AWS services on your behalf.

    • A new S3 bucket data-bucket is provisioned for the purpose of storing data. In a real-world scenario, you would typically point the crawler to an existing data store.

    • A glue_crawler resource is created and configured to scan the created S3 bucket. The s3_targets property is used to define the S3 path for the crawler. In this case, we dynamically construct the path using the bucket name.

    • The schedule property defines how often the crawler should run. In this example, the cron expression is set to trigger every day at 2 AM.

    • The program exports two values: the name of the S3 bucket and the ARN of the Glue Crawler, which can be useful for operational purposes and integration with other services or Pulumi stacks.

    Make sure to replace my_database with the name of a Glue Data Catalog database that exists in your AWS account or that you wish to be created when the crawler runs.

    To run this Pulumi program, save it in a Python file, e.g., glue_crawler.py, and execute it with the Pulumi CLI:

    pulumi up

    This command will provision the resources as defined in your Python program using the Pulumi Cloud Development Platform.