1. AI-powered ETL Workflows with AWS Glue


    AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it simple for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog.

    Once cataloged, your data is immediately searchable, queryable, and available for ETL. AWS Glue generates the code to execute your data transformations and data loading processes.

    In the context of Pulumi, we can create various AWS Glue resources to automate our ETL workflows. The core components to define in an ETL workflow generally include:

    1. Glue Data Catalog Database: The centralized metadata repository in AWS Glue.
    2. Glue Crawlers: To populate the Data Catalog with metadata table definitions.
    3. Glue Jobs: Execute the business logic of the data transformation.
    4. Glue Triggers: Schedule or event-based triggers to run ETL jobs.
    5. Glue Connections: To define connections to your data source.
    6. Glue Classifiers: To classify the data based on different formats.

    Let's create a basic ETL workflow using Pulumi and AWS Glue:

    import pulumi import pulumi_aws as aws # Create an AWS Glue Data Catalog database where metadata of processed data is stored glue_catalog_database = aws.glue.CatalogDatabase("my_catalog_database", name="my-catalog-database" ) # Define an AWS Glue Crawler to populate the AWS Glue Data Catalog # The data store can be an S3 bucket, a JDBC database, etc. glue_crawler = aws.glue.Crawler("my_crawler", name="my-crawler", database_name=glue_catalog_database.name, role=aws_iam_role.glue_service_role.arn, # IAM role with necessary permissions for Glue s3_targets=[{"path": "s3://my-glue-bucket/data-source/"}], # Sample S3 path as the data source ) # Define a Python shell job in AWS Glue glue_job = aws.glue.Job("my_job", name="my-python-shell-job", role_arn=aws_iam_role.glue_service_role.arn, # IAM role with necessary permissions for Glue glue_version="2.0", # The version of Glue to run this job command={ "name": "glueetl", # Use 'glueetl' for Spark ETL jobs, 'pythonshell' for Python Shell jobs "scriptLocation": "s3://my-glue-bucket/glue-scripts/my-etl-script.py" # S3 path of your ETL script }, # Set the default arguments for Glue job default_arguments={ "--additional-python-modules": "pandas==1.3.3, boto3==1.18.45" # for example, to install pandas and boto3 }, max_retries=2, # How many times to retry if the job fails timeout=30, # Timeout in minutes for the job run max_capacity=2.0 # The number of AWS Glue data processing units (DPUs) allocated to this Job run ) # Export the name of the database and the job pulumi.export("glue_catalog_database", glue_catalog_database.name) pulumi.export("glue_job_name", glue_job.name)

    In the code above, we first create an AWS Glue Data Catalog Database, a repository for metadata. Then, we define a Glue Crawler that will scan a specified data store (in our case, an S3 bucket) and populate data tables' metadata in our Data Catalog Database.

    Next, we create an AWS Glue Job to perform the actual data transformation. Here, we've chosen a Python shell job. This job references an IAM role that needs to have the right permissions for Glue operations and an ETL script stored in an S3 bucket.

    This forms a basic structure for building an ETL workflow using Pulumi and AWS Glue. You can add additional configurations such as triggers, security settings, and others as necessary for your workflow.

    Please replace placeholders such as IAM role ARN values and S3 paths with actual values from your environment. Make sure to have proper permissions set up in the IAM role for Glue to access the necessary resources.