Data Architecture for Data Engineers: Practical Approaches
3:50:31
Description
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.
Who is this for?
What You Need to Know?
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DescriptionUnlock 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.
Who this course is for:
- This course is ideal for Data Engineers, aspiring Data Architects, and Analytics Professionals who want to deepen their understanding of data architecture frameworks and practical applications. If you're a data professional looking to step into a strategic role by mastering data architecture, this course is designed for you.
- Who will benefit from this course:
- Early-career Data Engineers and Analysts aiming to advance their careers by building robust skills in data architecture principles, design, and cloud technologies.
- Aspiring Data Architects who want a comprehensive, practical foundation in data architecture concepts, including data modeling, data governance, and cloud-based data solutions.
- Tech Professionals in Data-Related Roles such as Business Intelligence (BI) engineers, Data Analysts, or Software Engineers who want to transition into data engineering or architecture roles.
- IT Managers and Team Leads looking to enhance their teams’ data capabilities and understand the broader architectural decisions impacting data strategy.
- Prior Knowledge Recommendations:
- Familiarity with Basic Data Concepts such as databases, data processing, and SQL will help learners maximize their experience.
- An Interest in Cloud Platforms like AWS, Azure, or Google Cloud is beneficial, but no advanced knowledge is required.
- Learners in this course will gain hands-on, practical insights into data architecture, positioning them to apply their knowledge immediately in data engineering roles or to transition toward data architecture.
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.
Who this course is for:
- This course is ideal for Data Engineers, aspiring Data Architects, and Analytics Professionals who want to deepen their understanding of data architecture frameworks and practical applications. If you're a data professional looking to step into a strategic role by mastering data architecture, this course is designed for you.
- Who will benefit from this course:
- Early-career Data Engineers and Analysts aiming to advance their careers by building robust skills in data architecture principles, design, and cloud technologies.
- Aspiring Data Architects who want a comprehensive, practical foundation in data architecture concepts, including data modeling, data governance, and cloud-based data solutions.
- Tech Professionals in Data-Related Roles such as Business Intelligence (BI) engineers, Data Analysts, or Software Engineers who want to transition into data engineering or architecture roles.
- IT Managers and Team Leads looking to enhance their teams’ data capabilities and understand the broader architectural decisions impacting data strategy.
- Prior Knowledge Recommendations:
- Familiarity with Basic Data Concepts such as databases, data processing, and SQL will help learners maximize their experience.
- An Interest in Cloud Platforms like AWS, Azure, or Google Cloud is beneficial, but no advanced knowledge is required.
- Learners in this course will gain hands-on, practical insights into data architecture, positioning them to apply their knowledge immediately in data engineering roles or to transition toward data architecture.
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Udemy
View courses Udemy- language english
- Training sessions 35
- duration 3:50:31
- Release Date 2025/03/09
