Data Architecture for Data Engineers: Practical Approaches

Description:

Unlock the potential of data architecture with Data Architecture for Data Engineers: Practical Approaches. This course is designed to give data engineers, aspiring data architects, and analytics professionals a solid foundation in creating scalable, efficient, and strategically aligned data solutions.

In this course, you’ll explore both traditional and modern data architectures, including data warehouses, data lakes, and the emerging data lakehouse approach. You'll learn about distributed and cloud-based architectures, along with practical applications of each to suit different data needs. We cover key aspects like data modeling, governance, and security, with emphasis on practical techniques for real-world implementation.

Starting with the foundational principles—data quality, scalability, security, and cost efficiency—we'll guide you through designing robust data pipelines, understanding ETL vs. ELT processes, and integrating batch and real-time data processing. With dedicated sections on AWS, Azure, and hybrid/multi-cloud architectures, you’ll gain hands-on insights into leveraging cloud tools for scalable data solutions.

This course also prepares you for a career transition, offering guidance on skills, certifications, and steps toward becoming a data architect. Through case studies, quizzes, and real-world examples, you’ll be equipped to make strategic architectural decisions and apply best practices across industries. By the end, you’ll have a comprehensive toolkit to design and implement efficient data architectures that align with business goals and emerging data needs.