Free download скачать Data Architecture For Data Engineers - Practical Approaches
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 773.24 MB | Duration: 3h 55m
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
Section 1: Introduction to the Instructor and Course Overview
Lecture 1 Meet Your Instructor
Lecture 2 Course Structure and Objectives
Section 2: Introduction to Data Architecture
Lecture 3 Key Tenets in Data Architecture and Governance
Lecture 4 Overview of Data Architecture
Lecture 5 Types of Data Architectures
Lecture 6 Monolithic Architecture
Lecture 7 Distributed Architecture
Lecture 8 Cloud-based Architecture Use Cases
Lecture 9 Choosing the Optimal Data Architecture
Lecture 10 Additional Readings
Section 3: Data Modeling for Effective Architectures
Lecture 11 Introduction to Data Modeling
Lecture 12 Database Types
Lecture 13 Database Design Approaches
Lecture 14 Normalization
Lecture 15 Denormalization
Lecture 16 Normalization & Denormalization - How to choose?
Lecture 17 Case Study
Lecture 18 Additional Readings
Section 4: Architecting Data Pipelines
Lecture 19 Introduction to Data Pipelines
Lecture 20 ETL vs. ELT Processes
Lecture 21 Data Pipeline Tools & Best Practices
Lecture 22 Batch Data Processing
Lecture 23 Real-time Data Processing
Lecture 24 Batch vs Real-time Data Processing
Lecture 25 Architecting Robust Pipelines - I
Lecture 26 Architecting Robust Pipelines - II
Lecture 27 Case Study
Lecture 28 Additional Readings
Section 5: Modern Data Architectures
Lecture 29 Data Lakes and Data Warehouses
Lecture 30 Data Lakehouse Architecture
Lecture 31 Data Mesh and Data Fabrics
Lecture 32 Case Study
Lecture 33 Additional Readings
Section 6: Cloud Data Architecture: Tools and Technologies
Lecture 34 AWS for Data Engineers
Lecture 35 Azure for Data Engineers
Lecture 36 Hybrid and Multi-cloud Architectures
Lecture 37 Additional Readings
Section 7: Cheat Sheet and Course Wrap-Up
Lecture 38 Step-by-Step Guide to Choosing an Architecture
Lecture 39 Road to Becoming a Data Architect
Lecture 40 Other Courses by Manas Jain
Lecture 41 Feedback & Course Conclusion
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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