Description

Introduction
This training focuses on how Python can be leveraged to build, automate, and optimize data integration and transformation workflows using AWS Glue and AWS Lambda. Participants gain hands-on skills in writing Python-based ETL scripts, orchestrating serverless data pipelines, managing schema evolution, and automating event-driven workflows. By the end, learners will be able to design intelligent, scalable, and fully automated serverless data integration solutions.

Prerequisites
Basic to intermediate Python programming
Familiarity with AWS (S3, IAM, Lambda basics preferred)
Understanding of data processing and ETL concepts

Table of Contents
1. Foundations of Python-Driven Serverless ETL
 1.1 Python’s Role in Modern Data Engineering
 1.2 Serverless ETL Concepts and Architecture
 1.3 Introduction to AWS Glue & Lambda
 1.4 Comparing Python ETL vs Spark ETL
 1.5 Key Use Cases for Python-Based Serverless Data Pipelines

2. AWS Glue Concepts & Components
 2.1 Data Catalog: Databases, Tables & Schemas
 2.2 Crawlers for Schema Discovery
 2.3 Glue Job Types: Spark, Python Shell & Streaming
 2.4 Glue Triggers, Workflows & Scheduling
 2.5 Security & IAM for Glue Operations

3. Python for Glue ETL Jobs
 3.1 Writing Python Shell Jobs in Glue
 3.2 Using DynamicFrames vs DataFrames
 3.3 Data Cleaning & Transformation Logic
 3.4 Handling Nested Data, Parquet, and JSON
 3.5 Python Libraries Integration in Glue Jobs

4. AWS Lambda with Python
 4.1 Python Runtime & Packaging Dependencies
 4.2 Configuring Event Triggers (S3, EventBridge, SNS, DynamoDB)
 4.3 Writing Python Lambda Handlers for ETL
 4.4 Error Handling, Logging & Observability
 4.5 Lambda Permissions, VPC Access & Security

5. Automating ETL with Glue & Lambda Integration
 5.1 Calling Glue Jobs from Lambda Using Python
 5.2 Passing Parameters & Processing Event Payloads
 5.3 Automating Crawler Runs with Lambda
 5.4 Chaining Multiple ETL Steps
 5.5 Creating Fully Automated Event-Driven ETL Pipelines

6. Data Connectivity & Storage Integration
 6.1 Reading/Writing Data from S3, DynamoDB & RDS
 6.2 Using Python to Connect to External JDBC Sources
 6.3 Data Partitioning & Performance Optimization
 6.4 Schema Evolution Handling in Glue
 6.5 Data Governance, Access & Encryption

7. Workflow Orchestration & Automation
 7.1 Glue Workflows for Pipeline Management
 7.2 EventBridge for Automation & Scheduling
 7.3 Using Step Functions for Multi-Step ETL
 7.4 CI/CD for Glue & Lambda (CodePipeline / SAM)
 7.5 Monitoring Pipelines with CloudWatch Alerts

8. Optimization, Performance & Cost Control
 8.1 Glue Performance Tuning Techniques
 8.2 Optimizing Lambda Execution with Python
 8.3 Reducing ETL Costs Using Serverless Best Practices
 8.4 Managing Large Data Volumes Efficiently
 8.5 Debugging ETL Failures & Logging Strategies

9. Real-World Project Implementation
 9.1 Designing a Serverless ETL Architecture with Python
 9.2 Building a Python-Based ETL Using Glue
 9.3 Automating Pipeline Execution Using Lambda
 9.4 Validating & Transforming Data in Real-Time
 9.5 End-to-End Production Deployment

This training empowers participants to automate and optimize ETL workflows using Python with AWS Glue and AWS Lambda. By mastering event-driven orchestration, Python scripting, and serverless best practices, learners can confidently deliver scalable, maintainable, and high-performance data integration pipelines in cloud environments.