Data Engineering on AWS
Official partner
AWS
Course Description
Through a balanced combination of theory, practical labs, and activities, participants learn to design, build, optimize, and secure data engineering solutions using AWS services. From foundational concepts to hands-on implementation of data lakes, data warehouses, and both batch and streaming data pipelines, this course equips data professionals with the skills needed to architect and manage modern data solutions at scale.
Course Summary
Module 1: Data Engineering Roles and Key Concepts
Module 2: AWS Data Engineering Tools and Services
Module 3: Designing and Implementing Data Lakes
Module 4: Optimizing and Securing a Data Lake Solution
Module 5: Data Warehouse Architecture and Design Principles
Module 6: Performance Optimization Techniques for Data Warehouses
Module 7: Security and Access Control for Data Warehouses
Module 8: Designing Batch Data Pipelines
Module 9: Implementing Strategies for Batch Data Pipeline
Module 10: Optimizing, Orchestrating, and Securing Batch Data Pipelines
Module 11: Streaming Data Architecture Patterns
Module 12: Optimizing and Securing Streaming Solutions
Prerequisites for this course
We recommend that attendees of this course have:
• Familiarity with basic machine learning concepts, such as supervised and unsupervised learning, regression, classification, and clustering algorithms.
• Working knowledge of Python programming language and common data science libraries like NumPy, Pandas, and Scikit-learn.
• Basic understanding of cloud computing concepts and familiarity with the AWS platform.
• Familiarity with SQL and relational databases is recommended but not mandatory.
• Experience with version control systems like Git is beneficial but not required.
Audience for this course
This course is designed for professionals who are interested in designing, building, optimizing, and securing data engineering solutions using AWS services.