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
Identifying ETL Requirements
Was this section helpful?
- Building the Data Warehouse, William H. Inmon, 2005 (John Wiley & Sons) - Offers a classical approach to data warehouse architecture and development, highlighting the importance of understanding business requirements.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - Discusses challenges in building data systems, including understanding data requirements, consistency, and reliability, relevant for ETL design.
- Data Quality: The Ten Big Problems and How to Avoid Them, Peter Aiken, 2017 (Technics Publications) - Addresses common data quality issues and methods for prevention and resolution, directly related to defining data quality requirements.