Data Engineering Fundamentals
Description
Foundation concepts of data engineering including data types, storage formats, processing patterns, and cloud computing principles.
Learning Objectives
Learners will understand fundamental data engineering concepts, differentiate between structured, semi-structured, and unstructured data, comprehend batch vs. streaming processing, and grasp essential cloud computing principles for data engineering.
Topics (8)
Comprehensive understanding of different data types, their properties, and appropriate use cases in data engineering.
Understanding various data storage formats, their advantages, disadvantages, and optimal use cases.
Understanding the differences between batch and real-time data processing, their trade-offs, and implementation considerations.
Comprehensive overview of data integration patterns, ETL vs ELT paradigms, and their implementation strategies.
Foundation concepts of cloud computing, service models (IaaS, PaaS, SaaS), and benefits for data engineering.
Introduction to data quality metrics, governance frameworks, and their importance in data engineering projects.
Overview of data engineering discipline, roles and responsibilities, and its importance in data-driven organizations.
Fundamentals of data pipeline design, architectural patterns, and best practices for building robust data workflows.