Data Engineering on AWS Training

Data Engineering on AWS is a 3-day intermediate course designed for professionals who want to master data engineering solutions using AWS services.

Through hands-on labs, theory, and real-world exercises, participants learn how to design, build, optimize, and secure modern data architectures — including data lakes, warehouses, and pipelines (batch and streaming).

The course prepares learners for the AWS Certified Data Engineer – Associate certification.

Participants should have:

• Basic understanding of machine learning concepts,

• Familiarity with Python and common data libraries (NumPy, Pandas, Scikit-learn),

• Knowledge of cloud computing and AWS fundamentals,

• Experience with SQL recommended,

• Familiarity with Git is beneficial.

Day 1

Module 1: Data Engineering Roles and Key Concepts

• Role of a Data Engineer
• Key functions of a Data Engineer
• Data Personas
• Data Discovery
• AWS Data Services

Module 2: AWS Data Engineering Tools and Services

• Orchestration and Automation
• Data Engineering Security
• Monitoring
• Continuous Integration and Continuous Delivery
• Infrastructure as Code
• AWS Serverless Application Model
• Networking Considerations
• Cost Optimization Tools

Module 3: Designing and Implementing Data Lakes

• Data lake introduction
• Data lake storage
• Ingest data into a data lake
• Catalog data
• Transform data
• Server data for consumption
• Hands-on lab: Setting up a Data Lake on AWS

Module 4: Optimizing and Securing a Data Lake Solution

• Open Table Formats
• Security using AWS Lake Formation
• Setting permissions with Lake Formation
• Security and governance
• Troubleshooting
• Hand-on lab: Automating Data Lake Creation using AWS Lake Formation Blueprints

Day 2

Module 5: Data Warehouse Architecture and Design Principles

• Introduction to data warehouses
• Amazon Redshift Overview
• Ingesting data into Redshift
• Processing data
• Serving data for consumption
• Hands-on Lab: Setting up a Data Warehouse using Amazon Redshift Serverless

Module 6: Performance Optimization Techniques for Data Warehouses

• Monitoring and optimization options
• Data optimization in Amazon Redshift
• Query optimization in Amazon Redshift
• Orchestration options

Module 7: Security and Access Control for Data Warehouses

• Authentication and access control in Amazon Redshift
• Data security in Amazon Redshift
• Auditing and compliance in Amazon Redshift
• Hands-on lab: Managing Access Control in Redshift

Module 8: Designing Batch Data Pipelines

• Introduction to batch data pipelines
• Designing a batch data pipeline
• AWS services for batch data processing

Module 9: Implementing Strategies for Batch Data Pipeline

• Elements of a batch data pipeline
• Processing and transforming data
• Integrating and cataloging your data
• Serving data for consumption
• Hands-on lab: A Day in the Life of a Data Engineer

Day 3

Module 10: Optimizing, Orchestrating, and Securing Batch Data Pipelines

• Optimizing the batch data pipeline
• Orchestrating the batch data pipeline
• Securing the batch data pipeline
• Hands-on lab: Orchestrating Data Processing in Spark using AWS Step Functions

Module 11: Streaming Data Architecture Patterns

• Introduction to streaming data pipelines
• Ingesting data from stream sources
• Streaming data ingestion services
• Storing streaming data
• Processing Streaming Data
• Analyzing Streaming Data with AWS Services
• Hands-on lab: Streaming Analytics with Amazon Managed Service for Apache Flink

Module 12: Optimizing and Securing Streaming Solutions

• Optimizing a streaming data solution
• Securing a streaming data pipeline
• Compliance considerations
• Hands-on lab: Access Control with Amazon Managed Streaming for Apache Kafka