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 Cleaning Basics
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- Data Quality: The Ten Essential Steps to Data Quality (2nd Edition), Thomas C. Redman, 2013 (Technics Publications) - Provides a practical framework for understanding and improving data quality, covering common issues and systematic approaches.
- Data Cleaning: Problems and Current Approaches, Ihab F. Ilyas and Xu Chu, 2019 IEEE Data Engineering Bulletin, Vol. 42 (IEEE) DOI: 10.1109/BDE.2019.00003 - A comprehensive survey of data cleaning challenges, techniques, and future directions, suitable for readers seeking an academic overview.
- Python for Data Analysis (3rd Edition), Wes McKinney, 2022 (O'Reilly Media) - An essential guide for practical data manipulation and cleaning using Python's Pandas library, including handling missing data and inconsistent formats.