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Master AWS Glue: Build Serverless ETL Pipelines Q&S Test
158 students

Master AWS Glue: Build Serverless ETL Pipelines Q&S Test

Master AWS Glue ETL, Data Catalog, & Crawlers. Build scalable Data Lakes using PySpark, DataBrew, and Blueprints.
Last updated 6/2026
English

What you'll learn

  • Master core AWS Glue components including Data Catalog, Crawlers, and Jobs to build robust, automated ETL pipelines from scratch. (132 chars)
  • Write and optimize PySpark scripts within Glue to transform massive datasets efficiently for data lakes and warehouses. (122 chars)
  • Implement Glue DataBrew and Interactive Sessions for rapid data preparation and exploratory data analysis without managing infrastructure. (134 chars)
  • Deploy production-ready workflows using Triggers and Blueprints to orchestrate complex data integration tasks with ease. (123 chars)

Included in This Course

585 questions
  • PRACTICE TEST 1101 questions
  • PRACTICE TEST 2100 questions
  • PRACTICE TEST 3100 questions
  • PRACTICE TEST 485 questions
  • PRACTICE TEST 599 questions
  • PRACTICE TEST 6100 questions

Description

Unlock the Power of Serverless Data Integration with AWS Glue

Data is the new oil, but only if you can refine it. In today’s cloud-driven world, AWS Glue has become the industry standard for serverless ETL (Extract, Transform, Load). This course is a comprehensive, hands-on guide designed to take you from a complete beginner to a confident Cloud Data Engineer capable of orchestrating complex data workflows.

Why Learn AWS Glue? Managing infrastructure for data processing is a thing of the past. AWS Glue allows you to focus on your data while AWS handles the scaling. Whether you are moving data into a Redshift warehouse or building a massive S3 Data Lake, Glue is the "connective tissue" that makes it possible.

What We Will Cover: In this course, we dive deep into the core ecosystem. You will start by mastering the AWS Glue Data Catalog and using Crawlers to automatically discover metadata. From there, we move into the heart of ETL: writing and optimizing PySpark scripts and using the Visual ETL editor for faster development.

We don’t just stop at the basics. You will explore advanced features like AWS Glue DataBrew for no-code data preparation and Interactive Sessions for real-time development. Finally, you will learn to orchestrate your entire pipeline using Triggers, Workflows, and Blueprints to ensure your data stays fresh and accurate.

By the end of this course, you will be able to:

  • Build end-to-end serverless ETL pipelines.

  • Automate schema discovery and metadata management.

  • Transform massive datasets using Spark without managing clusters.

  • Implement cost-saving strategies for cloud data processing.

Whether you are a Data Engineer looking to level up or an Analyst wanting to automate manual tasks, this course provides the practical skills needed to succeed in the modern data landscape. Enroll today and start building!

Who this course is for:

  • Data Engineers, Architects, and Analysts who want to automate their data integration workflows and master serverless ETL on the AWS ecosystem.