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  2. Creating AWS Glue Spark ETL Jobs Using Pulumi

How Do I Create AWS Glue Spark ETL Jobs Using Pulumi?

Introduction

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it simple to prepare and load your data for analytics. This guide will walk you through the process of setting up AWS Glue Spark ETL jobs using Pulumi, a modern infrastructure as code platform. By following these steps, you will automate the creation of necessary AWS resources such as IAM roles, security policies, Glue databases, and the Glue job itself.

Key Steps

  1. IAM Role and Policy: Establish an IAM role with the appropriate permissions to allow AWS Glue to access necessary resources, such as S3 buckets.
  2. Glue Database: Create a Glue database to organize your data within AWS Glue.
  3. Glue Job: Define the Glue ETL job, including the script and job properties.

Example Code

Here’s a complete example of setting up these resources using Pulumi and TypeScript:

import * as pulumi from "@pulumi/pulumi";
import * as aws from "@pulumi/aws";

// IAM Role for AWS Glue
const glueRole = new aws.iam.Role("glue_role", {
    name: "glue-role",
    assumeRolePolicy: JSON.stringify({
        Version: "2012-10-17",
        Statement: [{
            Effect: "Allow",
            Principal: {
                Service: "glue.amazonaws.com",
            },
            Action: "sts:AssumeRole",
        }],
    }),
});
// IAM Policy for Glue
const gluePolicy = new aws.iam.RolePolicy("glue_policy", {
    role: glueRole.id,
    policy: JSON.stringify({
        Version: "2012-10-17",
        Statement: [
            {
                Effect: "Allow",
                Action: [
                    "s3:ListBucket",
                    "s3:GetObject",
                    "s3:PutObject",
                ],
                Resource: [
                    "arn:aws:s3:::my-bucket",
                    "arn:aws:s3:::my-bucket/*",
                ],
            },
            {
                Effect: "Allow",
                Action: ["logs:*"],
                Resource: "arn:aws:logs:*:*:*",
            },
        ],
    }),
});
// Glue Database
const glueDatabase = new aws.glue.CatalogDatabase("glue_database", {name: "my_glue_database"});
// Glue ETL Job
const glueJob = new aws.glue.Job("glue_job", {
    name: "my_etl_job",
    roleArn: glueRole.arn,
    command: {
        name: "glueetl",
        scriptLocation: "s3://my-bucket/scripts/my-etl-script.py",
        pythonVersion: "3",
    },
    glueVersion: "3.0",
    maxCapacity: 10,
    timeout: 60,
});
export const glueRoleArn = glueRole.arn;
export const glueDatabaseName = glueDatabase.name;
export const glueJobName = glueJob.name;

Conclusion

This guide provided a step-by-step approach to creating an AWS Glue Spark ETL job using Pulumi. By defining the IAM roles, policies, Glue database, and ETL job in code, you can efficiently manage and automate your data processing workflows. This method not only streamlines the setup process but also ensures that your infrastructure is versioned and reproducible.

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