Holiday Tech Reading List: Books for Azure and AI Enthusiasts
The holidays are a perfect time to catch up on reading. Here’s my curated list of books for Azure developers, data engineers, and AI practitioners heading into 2023.
Cloud Architecture
”Designing Data-Intensive Applications” by Martin Kleppmann
The definitive guide to building reliable, scalable systems. Essential reading for anyone working with distributed data.
Key Topics:
- Data models and query languages
- Storage and retrieval
- Replication and partitioning
- Consistency and consensus
Why Read It: Every concept applies directly to Azure architecture decisions.
”Building Microservices” by Sam Newman
Practical guide to microservices architecture with real-world patterns.
Key Topics:
- Service decomposition
- Integration patterns
- Deployment strategies
- Monitoring and security
Azure Relevance: Maps directly to Azure Container Apps, AKS, and Service Bus patterns.
AI and Machine Learning
”Hands-On Machine Learning” by Aurelien Geron
The practical ML book - perfect for engineers getting into AI.
Key Topics:
- Scikit-learn fundamentals
- Neural networks with TensorFlow
- Deep learning architectures
- Production ML pipelines
Azure Relevance: Concepts transfer directly to Azure ML and Cognitive Services.
”Designing Machine Learning Systems” by Chip Huyen
Bridge the gap between ML research and production.
Key Topics:
- Data engineering for ML
- Feature engineering
- Model deployment
- Monitoring and maintenance
Why Now: As AI becomes central to development (hello, ChatGPT!), understanding ML systems is essential.
DevOps and Platform Engineering
”The Phoenix Project” by Gene Kim
The novel that started the DevOps revolution. A fun read that teaches profound lessons.
For Those Who’ve Read It: The sequel “The Unicorn Project” focuses on the developer experience.
”Team Topologies” by Matthew Skelton and Manuel Pais
The book behind platform engineering thinking.
Key Topics:
- Team interaction modes
- Conway’s Law in practice
- Platform teams
- Cognitive load
Why It Matters: Essential reading for anyone building or using internal platforms.
”Accelerate” by Nicole Forsgren, Jez Humble, Gene Kim
The research behind DevOps performance.
Key Topics:
- DORA metrics
- What high performers do differently
- Technical and cultural practices
Why Read It: Data-driven guide to improving engineering effectiveness.
Software Craft
”A Philosophy of Software Design” by John Ousterhout
Deep thinking about software complexity.
Key Topics:
- Complexity causes and symptoms
- Module design
- Comments and documentation
- Consistency in design
Short but Impactful: Can read in a weekend but will change how you think.
”Software Engineering at Google” by Titus Winters et al.
Lessons from Google’s engineering practices.
Key Topics:
- Code review
- Testing
- Documentation
- Large-scale changes
Why It’s Different: Real practices from a company operating at massive scale.
Data Engineering
”Fundamentals of Data Engineering” by Joe Reis and Matt Housley
The comprehensive guide to modern data engineering.
Key Topics:
- Data generation and storage
- Ingestion and transformation
- Serving and analytics
- Security and governance
Azure Relevance: Covers patterns implemented by Synapse, Data Factory, and Fabric.
”The Data Warehouse Toolkit” by Ralph Kimball
The classic dimensional modeling book - still relevant.
Why Still Read It: Dimensional modeling fundamentals apply regardless of technology.
Business and Career
”Staff Engineer” by Will Larson
Guide to the technical leadership track.
Key Topics:
- What staff engineers do
- Operating at staff level
- Getting to staff level
Why Read It: Career growth beyond senior engineer.
”The Manager’s Path” by Camille Fournier
If you’re considering management or want to understand your manager.
Key Topics:
- Tech lead to CTO progression
- Managing teams and managers
- Building culture
Bonus: Written by someone who deeply understands engineering.
Reading Strategies
reading_approach = {
"deep_read": {
"books": [
"Designing Data-Intensive Applications",
"A Philosophy of Software Design"
],
"approach": "Read slowly, take notes, implement concepts",
"time": "1-2 months each"
},
"practical_read": {
"books": [
"Hands-On Machine Learning",
"Building Microservices"
],
"approach": "Read with laptop, do exercises",
"time": "2-3 weeks"
},
"reference_read": {
"books": [
"Software Engineering at Google",
"The Data Warehouse Toolkit"
],
"approach": "Read relevant chapters, return as needed",
"time": "Ongoing"
},
"weekend_read": {
"books": [
"The Phoenix Project",
"Staff Engineer"
],
"approach": "Read for enjoyment and insight",
"time": "2-3 days"
}
}My 2023 Reading Plan
- Q1: “Designing Machine Learning Systems” - ChatGPT makes this timely
- Q2: Deep re-read of “Designing Data-Intensive Applications”
- Q3: “Staff Engineer” and related career books
- Q4: Whatever emerges as important mid-year
Free Resources
If buying books isn’t in the budget:
- Microsoft Learn - Comprehensive Azure documentation
- Azure Architecture Center - Real-world patterns
- O’Reilly Safari - Often included with company subscriptions
- Local Library - Many have technical books
Conclusion
The best technical books aren’t about specific technologies - they’re about principles that transfer across tools and platforms. ChatGPT can explain syntax; these books help you understand why.
Happy holidays and happy reading!
Full Reading List
| Book | Author | Focus Area |
|---|---|---|
| Designing Data-Intensive Applications | Kleppmann | Architecture |
| Building Microservices | Newman | Architecture |
| Hands-On Machine Learning | Geron | ML/AI |
| Designing Machine Learning Systems | Huyen | ML/AI |
| The Phoenix Project | Kim | DevOps |
| Team Topologies | Skelton/Pais | Platform Engineering |
| Accelerate | Forsgren et al. | DevOps |
| A Philosophy of Software Design | Ousterhout | Software Craft |
| Software Engineering at Google | Winters et al. | Engineering Practice |
| Fundamentals of Data Engineering | Reis/Housley | Data |
| Staff Engineer | Larson | Career |
| The Manager’s Path | Fournier | Career |