Master AWS Lambda Functions for Data Engineers Using Python
 
Course Description
Master AWS Lambda Functions for Data Engineers Using Python is a hands-on, practical training program designed to teach data engineers, cloud practitioners, and automation specialists how to build scalable, serverless data pipelines using AWS Lambda. This course provides an in-depth understanding of how to create event-driven workflows, process data at scale, and integrate Lambda with core AWS services using Python.
 
You’ll learn how to write efficient Lambda functions, trigger them with event sources such as S3, Kinesis, DynamoDB, and EventBridge, and build end-to-end data processing pipelines without managing servers. The course also covers IAM permissions, networking, monitoring, error handling, environment variables, packaging dependencies, and optimizing performance and cost.
 
Through real-world examples and guided labs, you’ll develop the skills to build production-ready serverless data engineering solutions using Python and AWS-native tools.
 
What You’ll Learn
 
Lambda Fundamentals
Understanding serverless architecture and AWS Lambda concepts
Authoring Lambda functions using Python
Packaging, deploying, and versioning Lambda functions
 
Event-Driven Data Engineering
Building event-driven pipelines with S3, DynamoDB Streams, Kinesis, and EventBridge
Automating ETL/ELT processing using serverless components
Connecting Lambda with Glue, Redshift, SQS, SNS, and Step Functions
 
Serverless Data Processing
Handling batch and streaming data processing workloads
Parsing, transforming, enriching, and routing data with Lambda
Integrating Lambda with data lakes and warehouse architectures
Security & Access Control
Implementing least-privilege IAM roles and policies
Managing secrets with AWS Secrets Manager and Parameter Store
VPC configurations and private networking for Lambda functions
 
Optimization & Reliability
Improving performance using concurrency settings and function tuning
Cost optimization strategies for high-volume workloads
Observability with CloudWatch Logs, Metrics, and X-Ray
Error handling, retries, DLQs, and fault-tolerant design patterns
 
Who This Course Is For
Data engineers working with AWS
Python developers building serverless applications
Cloud engineers designing automated data workflows
ETL/ELT practitioners modernizing their pipelines
Anyone building event-driven, serverless data engineering solutions