Growth Engineering How to Build Systems That Drive Product Success in an AI-Driven World.
Build software that users actually use with proven growth-oriented software development strategies In Growth Engineering: How to Build Systems That Drive Product Success in an AI-Driven World, experienced software engineer with the Microsoft Experiences + Devices Growth team, Rita Okonkwo, delivers...
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| Format: | eBook |
| Language: | English |
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Newark :
John Wiley & Sons, Incorporated,
2026.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- Cover
- Title Page
- Copyright
- About the Author
- Acknowledgments
- Contents
- Preface
- Foreword
- Introduction
- Why Now
- Why This Book
- Chapter 1 Growth Engineering
- The Role of Engineers in Product Growth
- Key Growth Strategies
- Habit Formation
- Freemium Model
- Experimentation
- Data-Driven Growth
- Chapter 2 Observability
- Instrumentation
- How to Know What to Instrument
- Legal and Compliance Checklist
- A Practical Example of Instrumentation
- Telemetry
- Logs
- Metrics
- Traces
- Implementing Observability in Practice
- Defining the Signals
- Understanding the Flow
- Using Observability to Act
- Making It a Habit
- Observability Anti-Patterns
- Tracking Everything Without Purpose
- Logging Without Context
- Relying Only on Logs
- Instrumenting Too Late
- No Clear Ownership
- Tools for Observability
- What This Chapter Covered
- Key Questions for Reflection
- Exercise
- Chapter 3 Data Pipelines
- What Is a Data Pipeline and Why Does It Matter?
- Components of a Data Pipeline
- Ingestion
- Batch Ingestion
- Streaming Ingestion
- Transportation
- Message Brokers or Queues
- Streaming Platforms or Distributed Logs
- Telemetry Forwarders or Data Shippers
- Processing
- Keep It Simple at First
- Validate Early
- Make It Observable
- Use Version Control for Logic
- Storage
- Data Warehouses
- Data Lakes
- When to Use What
- Visualization
- Tools and Interfaces
- Types of Visualizations and When to Use Them
- Building a Growth Pipeline with Large Language Models
- Step 1: Define the Role or Persona
- Step 2: Define What You Want to Measure
- Step 3: Instrumentation Strategy
- Step 4: Generate Mock Data
- Step 5: Process Data
- Step 6: Store Data
- Step 7: Visualize Data
- What This Chapter Covered
- Key Questions for Reflection
- Exercise
- Chapter 4 Data Modeling
- OLTP vs. OLAP
- OLTP
- OLAP
- Modeling for OLTP
- How to Create an ER Diagram
- Understanding Cardinality
- One-to-One(1:1)
- One-to-Many(1:N)
- Many-to-Many(N:M)
- Building an ER Diagram for a Growth Use Case
- Step 1: Identify Your Entities
- Step 2: Define the Relationships
- Step 3: Add Attributes
- Step 4: Diagram It Out
- Step 5: Think Through Growth Questions
- Step 6: Avoid Modeling Pitfalls
- Step 7: Get Ready for the Next Layer
- Normalization
- What Is a Relation?
- Keys: Primary, Foreign, and Composite
- Functional Dependencies
- Normalization
- Modeling for OLAP
- Facts and Dimensions
- Denormalization
- Star and Snowflake Schemas
- Star Schema
- Snowflake Schema
- Choosing Between Them
- What This Chapter Covered
- Key Questions for Reflection
- Exercise
- Chapter 5 What Are Experiments?
- The Philosophy of Experimentation
- Humility in Product Development
- Experimentation as a Team Sport
- Experimentation Protects Users
- The Anatomy of an Experiment
- Hypothesis Formation
- Control and Treatment Groups
- Randomization