AWS Glue ETL Overview

Overview

This lecture provides a detailed introduction to AWS Glue ETL, explains its key features, and demonstrates how to create and run a basic ETL job using the visual interface.

Introduction to AWS Glue ETL

• ETL stands for Extract, Transform, and Load, crucial in data engineering for moving and processing data.
• AWS Glue ETL is a fully managed service by AWS for performing ETL operations.
• Glue ETL eliminates the need for managing servers and infrastructure.

Features of AWS Glue ETL

• Fully managed: AWS handles all hardware, scaling, and software updates.
• Serverless: No need to provision or manage servers.
• Integrated with Apache Spark for scalable big data processing.
• Multiple development options: visual (drag-and-drop), code-based (PySpark scripts), and interactive notebooks.
• Built-in scheduling, orchestration, and monitoring of ETL workflows.
• Easy connectivity with various AWS and external data sources (e.g., S3, Redshift, RDS).

Creating a Visual ETL Job in AWS Glue

• Example use case: Transform customer data from one S3 folder to another.
• Requirements: Input data in S3 and an IAM role with S3, Glue, and CloudWatch permissions.
• Visual ETL allows users to set source, apply transformations, and define targets via a UI.
• Source setup: Select S3 location or Glue Data Catalog table; preview data if needed.
• Transformation example: Dropping a specific field (e.g., last name) from the CSV.
• Target setup: Write transformed data to another S3 path in Parquet format with compression.

Running and Monitoring the Job

• Glue automatically generates an ETL script based on UI selections.
• Job execution is managed by AWS without manual server setup.
• Processing resources can be adjusted (e.g., DPU, worker type).
• Job status and logs can be monitored within the AWS Glue console.

Key Terms & Definitions

• ETL — Extract, Transform, Load: process of moving and transforming data between systems.
• Fully managed — AWS handles infrastructure, scaling, and software for you.
• Serverless — No need to maintain or deploy servers.
• Apache Spark — Distributed processing framework included in Glue for big data tasks.
• Dynamic Frame — Glue’s abstraction for handling datasets, especially for semi-structured data.
• IAM Role — AWS Identity and Access Management role that grants permissions for Glue jobs.

Action Items / Next Steps

• Create an IAM role with S3, Glue, and CloudWatch permissions for your ETL jobs.
• Prepare your sample data in S3 and optionally set up a Glue Data Catalog table.
• Practice creating a Glue ETL job using the visual interface as demonstrated.
• Read output files in the target location to verify the applied transformations.
• Prepare for upcoming lessons on developing ETL jobs using PySpark scripts.