Data Architecture For Data Engineers: Practical Approaches

Building Scalable, Efficient Data Solutions with Real-World Applications

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.

Requirements
Basic Understanding of Data Concepts: Familiarity with data structures, databases, and general data processing will make it easier to follow along with the technical aspects.
Knowledge of SQL and Data Storage: Some experience with SQL and an understanding of different types of data storage (e.g., relational databases, cloud storage) would be helpful, though not essential.
Interest in Data Architecture and Cloud Platforms: Curiosity about data architecture frameworks and cloud platforms like AWS, Azure, or Google Cloud will make the course content more engaging and relevant.
No specific tools or advanced skills are required for beginners; the course is designed to introduce you to key concepts and guide you through practical data architecture approaches step-by-step. If you're motivated to learn and eager to apply new skills, this course is for you!

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.

Overview