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
Handling Loading Failures
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- The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, Ralph Kimball and Margy Ross, 2013 (John Wiley & Sons) - This foundational book covers the design and implementation of data warehouses, including comprehensive discussions on ETL processes, data quality management, and strategies for handling exceptions and errors during data loading.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - This book explores the fundamental principles of building reliable, scalable, and maintainable data systems, with relevant chapters on distributed transactions, fault tolerance, and dealing with system failures that apply directly to robust ETL pipeline design.
- Loading Data - Error Handling, Snowflake Inc., 2024 (Snowflake Documentation) - Official documentation describing various strategies and features for handling errors during data loading into Snowflake, including options for
ON_ERRORbehavior, validating data, and managing rejected records. - Fundamentals of Data Engineering, Joe Reis, Matt Housley, 2022 (O'Reilly Media) - A modern guide to data engineering principles and practices, including detailed sections on building resilient data pipelines, ensuring data quality, and implementing effective error handling and monitoring strategies for loading data.