Introduction to Data Engineering
Chapter 1: What is Data Engineering?
Defining Data Engineering
The Role of a Data Engineer
Data Engineering vs Data Science vs Data Analysis
The Data Lifecycle
Common Data Engineering Tasks
Why Data Engineering Matters for AI
Quiz for Chapter 1
Chapter 2: Foundational Concepts
Understanding Data Types
Data Sources and Collection Methods
Introduction to Databases
Data Warehouses Explained
Data Lakes Explained
Introduction to APIs for Data Retrieval
Hands-on Practical: Identifying Data Types
Quiz for Chapter 2
Chapter 3: Building Your First Data Pipeline
What is a Data Pipeline?
ETL Process Explained
ELT Process Explained
Data Extraction Techniques
Basic Data Transformation Operations
Loading Data into Storage
Simple Pipeline Orchestration Concepts
Practice: Sketching a Basic Pipeline
Quiz for Chapter 3
Chapter 4: Data Storage Fundamentals
Choosing the Right Data Storage
Working with Relational Databases (SQL Basics)
Introduction to NoSQL Databases
Understanding File Storage Systems
Object Storage Basics
Common Data Formats
Practice: Setting up a Simple Database Table
Quiz for Chapter 4
Chapter 5: Introduction to Data Processing
Batch Processing Explained
Stream Processing Explained
Processing Frameworks Overview
Understanding Compute Resources
Data Cleaning Basics
Data Validation Techniques
Practice: Simple Data Cleaning Script
Quiz for Chapter 5
Chapter 6: Essential Tools for Data Engineers
Introduction to SQL for Data Manipulation
Version Control with Git for Code
Command-Line Interface (CLI) Basics
Overview of Cloud Platforms
Introduction to Workflow Schedulers
Practice: Basic Git Commands
Quiz for Chapter 6
Chapter 7: Next Steps in Data Engineering
Areas for Further Learning
Building a Portfolio Project Idea
Contributing to Open Source
Keeping Up with New Tools
Recap of Course Concepts
Quiz for Chapter 7
Data Engineering vs Data Science vs Data Analysis
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- Fundamentals of Data Engineering, Joe Reis, Matt Housley, 2022 (O'Reilly Media) - This book offers a clear introduction to the field of data engineering, systematically defining its principles and differentiating the role from data analysis and data science.
- An Introduction to Statistical Learning: With Applications in Python, Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, Jonathan Taylor, 2023 (Springer) DOI: 10.1007/978-3-031-38870-1 - A highly regarded textbook covering fundamental statistical learning and machine learning concepts, essential for understanding the methods employed in data science.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - Offers a deep review of the engineering principles required to build and maintain data systems, providing context for the infrastructure and pipelines data engineers create.
- The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd Edition), Ralph Kimball, Margy Ross, 2013 (Wiley) - A foundational resource for understanding data warehousing concepts and dimensional modeling, which are critical for structuring data used by data analysts and scientists.