Table of contents

Data Engineering on AWS

From fundamentals to production-grade pipelines, analytics platforms, PySpark, SQL, and observability with Datadog

Read each section in order. Every title can be opened as a TheoryTrace document.

• Cover
• Copyright
• How to read this book
• Introduction
• Chapter 1: The Data Engineering Mindset
• Chapter 2: Data Fundamentals: Files, Tables, Events, and Schemas
• Chapter 3: SQL for Data Engineering
• Chapter 4: Python for Data Pipelines
• Chapter 5: Linux, Networking, and Cloud Foundations
• Chapter 6: AWS Core Services for Data Engineers
• Chapter 7: Amazon S3 as a Data Lake Foundation
• Chapter 8: Data Modeling for Analytics
• Chapter 9: Batch Ingestion Patterns
• Chapter 10: Streaming and Event-Driven Data Engineering
• Chapter 11: Apache Spark and PySpark Fundamentals
• Chapter 12: Production PySpark Transformations
• Chapter 13: AWS Glue and EMR for Distributed Processing
• Chapter 14: Querying the Lake with Athena and Redshift
• Chapter 15: Orchestration and Workflow Design
• Chapter 16: Data Quality, Testing, and Reliability
• Chapter 17: Observability for Data Systems with Datadog
• Chapter 18: Security, Governance, and Compliance
• Chapter 19: CI/CD and Infrastructure as Code for Data Engineering
• Chapter 20: Cost, Performance, and Scalability
• Chapter 21: Building an End-to-End AWS Data Platform
• Chapter 22: Career-Ready Data Engineering Practice
• Conclusion