Data Architecture for Data Engineers: Practical Approaches

Unlock the potential of data architecture with <b>Data Architecture for Data Engineers: Practical Approache</b>s. 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.<div><br><div></div><div>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. </div><div><br></div><div>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. </div><div><br></div><div>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.</div><div> </div><div> </div></div>

What you'll learn:

• Evaluate and select data architectures based on specific business needs and data characteristics.
• Design data models and implement database strategies for structured and unstructured data.
• Build scalable, fault-tolerant data pipelines using ETL/ELT processes and real-time data processing.
• Implement cloud-based data solutions on AWS, Azure, and multi-cloud environments.
• Differentiate between modern data architectures, including data lakes, warehouses, and lakehouses, for optimal data storage.
• Apply best practices for data governance, security, and compliance within data architecture frameworks.
• Analyze and choose appropriate data integration and management tools for hybrid and multi-cloud strategies.
• Plan a career path from Data Engineer to Data Architect, including key skills and certifications.