Description

Introduction
This training provides a complete understanding of how to design, build and automate serverless ETL pipelines using AWS Glue and AWS Lambda with Python. Participants learn to orchestrate data extraction, transformation, and loading workflows while leveraging Glue’s data cataloging, job management, and Lambda’s event-driven compute power. By the end of the course, learners gain the skills to build scalable, cost-efficient, and fully automated serverless data integration solutions.

Prerequisites
Basic Python programming knowledge
Understanding of cloud concepts
Familiarity with AWS basics (S3, IAM, CloudWatch preferred)

Table of Contents
1. Introduction to Serverless ETL with AWS
 1.1 Understanding Serverless Architecture
 1.2 Overview of ETL and Data Integration Concepts
 1.3 AWS Glue and Lambda in the Modern Data Stack
 1.4 Comparing Serverless ETL vs Traditional ETL
 1.5 Use Cases and Business Benefits

2. AWS Glue Fundamentals
 2.1 AWS Glue Data Catalog Essentials
 2.2 Creating and Managing Databases and Tables
 2.3 Crawlers: Configuration and Automation
 2.4 Glue Job Types: Spark, Python Shell, and Streaming
 2.5 Glue Connections, Endpoints, and Security

3. Python for AWS Glue Development
 3.1 Writing Python Scripts for Glue ETL Jobs
 3.2 Working with DynamicFrames and PySpark
 3.3 Data Cleaning, Transformations & Joins
 3.4 Converting Between DataFrames and DynamicFrames
 3.5 Handling Schema Inference and Schema Evolution

4. AWS Lambda Essentials
 4.1 Lambda Architecture and Execution Model
 4.2 Python Runtime and Packaging Dependencies
 4.3 Event Triggers: S3, EventBridge, DynamoDB, SNS
 4.4 Managing Lambda IAM Roles & Permissions
 4.5 Logging, Monitoring & Error Handling

5. Integrating Glue & Lambda for Serverless ETL Pipelines
 5.1 Invoking Glue Jobs from Lambda
 5.2 Passing Parameters and Processing Events
 5.3 Orchestrating Multi-Step Workflows
 5.4 Triggering Glue Crawlers Automatically
 5.5 Building End-to-End Automated Pipelines

6. Data Storage & Connectivity in AWS ETL
 6.1 Using Amazon S3 as a Data Lake
 6.2 Working with Redshift, RDS, and DynamoDB
 6.3 Connecting Glue Jobs to JDBC Data Sources
 6.4 Best Practices for Data Partitioning
 6.5 Securing Data with IAM, VPC & KMS

7. Workflow Automation & Orchestration
 7.1 Using AWS Glue Workflows and Triggers
 7.2 EventBridge Rules for Serverless Scheduling
 7.3 Step Functions for Complex ETL Orchestration
 7.4 Monitoring Pipeline Health & Automating Alerts
 7.5 CI/CD for ETL with CodeCommit & CodePipeline

8. Optimization & Cost Management
 8.1 Optimizing Glue Job Performance
 8.2 Managing Lambda Concurrency & Timeouts
 8.3 Reducing ETL Costs through Serverless Best Practices
 8.4 Handling Large Datasets Efficiently
 8.5 Debugging and Troubleshooting Techniques

9. Real-World Project Implementation
 9.1 Designing a Production-Ready ETL Architecture
 9.2 Building a Serverless ETL Pipeline from Scratch
 9.3 Automating Catalog Updates and Data Transformations
 9.4 Deploying Lambda-Triggered Glue Jobs
 9.5 End-to-End Pipeline Demo in a Real Scenario

This training equips participants with the skills to build scalable, automated, and cost-optimized serverless ETL pipelines using AWS Glue, AWS Lambda, and Python. By mastering how these services work together, learners can deliver modern, efficient, and production-ready data integration solutions for any enterprise environment.