Introduction to ETL Pipelines
Chapter 1: Understanding ETL Fundamentals
What is Data Integration?
Introducing ETL: Extract, Transform, Load
The Purpose of ETL Processes
Distinguishing ETL from ELT
Common Data Sources and Targets
Identifying ETL Requirements
Quiz for Chapter 1
Chapter 2: The Extraction Stage
Connecting to Data Sources
Full Extraction vs. Incremental Extraction
Working with Structured Data (e.g., Databases, CSV)
Introduction to Semi-Structured Data (e.g., JSON, XML)
Change Data Capture (CDC) Concepts
Handling Extraction Errors
Practice: Simulating Data Extraction
Quiz for Chapter 2
Chapter 3: The Transformation Stage
Why Data Transformation is Necessary
Data Cleaning: Handling Missing Values
Data Cleaning: Correcting Errors
Data Validation Rules
Data Formatting and Standardization
Data Enrichment: Adding Information
Data Structuring: Joining and Splitting Data
Introduction to Data Aggregation
Practice: Applying Simple Transformations
Quiz for Chapter 3
Chapter 4: The Loading Stage
Choosing a Target System
Loading Strategies: Full Load
Loading Strategies: Incremental Load (Append/Update)
Understanding Target Schemas
Schema Mapping: Source to Target
Handling Loading Failures
Data Validation Post-Load
Practice: Data Loading
Quiz for Chapter 4
Chapter 5: Building Simple ETL Pipelines
What is an ETL Pipeline?
Pipeline Workflow and Dependencies
Introduction to ETL Tools Categories
Visual ETL Tools Overview
Scripting for ETL Overview
Scheduling and Automation Basics
Monitoring and Logging Fundamentals
Hands-on Practical: Designing a Simple Pipeline
Quiz for Chapter 5
Data Cleaning: Handling Missing Values
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- Statistical Analysis with Missing Data, Roderick J. A. Little and Donald B. Rubin, 2014 (John Wiley & Sons) DOI: 10.1002/9781119013563 - This is a definitive work on the theory and methods for handling missing data, covering mechanisms of missingness and various imputation techniques.
- Python for Data Analysis, Wes McKinney, 2022 (O'Reilly Media) - Provides practical guidance on data wrangling with Python's pandas library, including essential techniques for identifying and handling missing values.
- The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, Ralph Kimball and Margy Ross, 2013 (John Wiley & Sons) - A standard text for ETL and data warehousing, which discusses the role of data quality and cleaning during the transformation stage of an ETL pipeline.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media) - Contains practical examples of data preprocessing, including simple strategies like mean/median imputation for missing values, relevant for preparing data for analytical models.