فهرست مطالب

• Preface
• I. Foundation and Building Blocks
• Chapter 1: Data Engineering Described
• What Is Data Engineering?
• Data Engineering Skills and Activities
• Data Engineers Inside an Organization
• Conclusion
• Additional Resources
• Chapter 2: The Data Engineering Lifecycle
• What Is the Data Engineering Lifecycle?
• Major Undercurrents Across the Data Engineering Lifecycle
• Conclusion
• Additional Resources
• Chapter 3: Designing Good Data Architecture
• What Is Data Architecture?
• Principles of Good Data Architecture
• Major Architecture Concepts
• Examples and Types of Data Architecture
• Who’s Involved with Designing a Data Architecture?
• Conclusion
• Additional Resources
• Chapter 4: Choosing Technologies Across the Data Engineering Lifecycle
• Team Size and Capabilities
• Speed to Market
• Interoperability
• Cost Optimization and Business Value
• Today Versus the Future: Immutable Versus Transitory Technologies
• Location
• Build Versus Buy
• Monolith Versus Modular
• Serverless Versus Servers
• Optimization, Performance, and the Benchmark Wars
• Undercurrents and Their Impacts on Choosing Technologies
• Conclusion
• Additional Resources
• II. The Data Engineering Lifecycle in Depth
• Chapter 5: Data Generation in Source Systems
• Sources of Data: How Is Data Created?
• Source Systems: Main Ideas
• Source System Practical Details
• Whom You’ll Work With
• Undercurrents and Their Impact on Source Systems
• Conclusion
• Additional Resources
• Chapter 6: Storage
• Raw Ingredients of Data Storage
• Data Storage Systems
• Data Engineering Storage Abstractions
• Big Ideas and Trends in Storage
• Whom You’ll Work With
• Undercurrents
• Conclusion
• Additional Resources
• Chapter 7: Ingestion
• What Is Data Ingestion?
• Key Engineering Considerations for the Ingestion Phase
• Batch Ingestion Considerations
• Message and Stream Ingestion Considerations
• Ways to Ingest Data
• Whom You’ll Work With
• Undercurrents
• Conclusion
• Additional Resources
• Chapter 8: Queries, Modeling, and Transformation
• Queries
• Data Modeling
• Transformations
• Whom You’ll Work With
• Undercurrents
• Conclusion
• Additional Resources
• Chapter 9: Serving Data for Analytics, Machine Learning, and Reverse ETL
• General Considerations for Serving Data
• Analytics
• Machine Learning
• What a Data Engineer Should Know About ML
• Ways to Serve Data for Analytics and ML
• Reverse ETL
• Whom You’ll Work With
• Undercurrents
• Conclusion
• Additional Resources
• III. Security, Privacy, and the Future of Data Engineering
• Chapter 10: Security and Privacy
• People
• Processes
• Technology
• Conclusion
• Additional Resources
• Chapter 11: The Future of Data Engineering
• The Data Engineering Lifecycle Isn’t Going Away
• The Decline of Complexity and the Rise of Easy-to-Use Data Tools
• The Cloud-Scale Data OS and Improved Interoperability
• “Enterprisey” Data Engineering
• Titles and Responsibilities Will Morph...
• Moving Beyond the Modern Data Stack, Toward the Live Data Stack
• Conclusion
• Appendix A: Serialization and Compression Technical Details
• Serialization Formats
• Database Storage Engines
• Compression: gzip, bzip2, Snappy, Etc.
• Appendix B: Cloud Networking
• Cloud Network Topology
• CDNs
• The Future of Data Egress Fees
• Index
• About the Authors