Fundamentals of Data Engineering

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• Preface
  • What This Book Isnt
  • What This Book Is About
  • Who Should Read This Book
  • Prerequisites
  • What Youll Learn and How It Will Improve Your Abilities
  • Navigating This Book
  • Conventions Used in This Book
  • How to Contact Us
  • Acknowledgments
• I. Foundation and Building Blocks
• 1. Data Engineering Described
  • What Is Data Engineering?
    • Data Engineering Defined
    • The Data Engineering Lifecycle
    • Evolution of the Data Engineer
      • The early days: 1980 to 2000, from data warehousing to the web
      • The early 2000s: The birth of contemporary data engineering
      • The 2000s and 2010s: Big data engineering
      • The 2020s: Engineering for the data lifecycle
    • Data Engineering and Data Science
  • Data Engineering Skills and Activities
    • Data Maturity and the Data Engineer
      • Stage 1: Starting with data
      • Stage 2: Scaling with data
      • Stage 3: Leading with data
    • The Background and Skills of a Data Engineer
    • Business Responsibilities
    • Technical Responsibilities
    • The Continuum of Data Engineering Roles, from A to B
  • Data Engineers Inside an Organization
    • Internal-Facing Versus External-Facing Data Engineers
    • Data Engineers and Other Technical Roles
      • Upstream stakeholders
        • Data architects
        • Software engineers
        • DevOps engineers and site-reliability engineers
      • Downstream stakeholders
        • Data scientists
        • Data analysts
        • Machine learning engineers and AI researchers
    • Data Engineers and Business Leadership
      • Data in the C-suite
        • Chief executive officer
        • Chief information officer
        • Chief technology officer
        • Chief data officer
        • Chief analytics officer
        • Chief algorithms officer
      • Data engineers and project managers
      • Data engineers and product managers
      • Data engineers and other management roles
  • Conclusion
  • Additional Resources
• 2. The Data Engineering Lifecycle
  • What Is the Data Engineering Lifecycle?
    • The Data Lifecycle Versus the Data Engineering Lifecycle
    • Generation: Source Systems
      • Evaluating source systems: Key engineering considerations
    • Storage
      • Evaluating storage systems: Key engineering considerations
      • Understanding data access frequency
      • Selecting a storage system
    • Ingestion
      • Key engineering considerations for the ingestion phase
      • Batch versus streaming
      • Key considerations for batch versus stream ingestion
      • Push versus pull
    • Transformation
      • Key considerations for the transformation phase
    • Serving Data
      • Analytics
        • Business intelligence
        • Operational analytics
        • Embedded analytics
      • Machine learning
      • Reverse ETL
  • Major Undercurrents Across the Data Engineering Lifecycle
    • Security
    • Data Management
      • Data governance
        • Discoverability
        • Metadata
        • Data accountability
        • Data quality
      • Data modeling and design
      • Data lineage
      • Data integration and interoperability
      • Data lifecycle management
      • Ethics and privacy
    • DataOps
      • Automation
      • Observability and monitoring
      • Incident response
      • DataOps summary
    • Data Architecture
    • Orchestration
    • Software Engineering
      • Core data processing code
      • Development of open source frameworks
      • Streaming
      • Infrastructure as code
      • Pipelines as code
      • General-purpose problem solving
  • Conclusion
  • Additional Resources
• 3. Designing Good Data Architecture
  • What Is Data Architecture?
    • Enterprise Architecture Defined
      • TOGAFs definition
      • Gartners definition
      • EABOKs definition
      • Our definition
    • Data Architecture Defined
      • TOGAFs definition
      • DAMAs definition
      • Our definition
    • Good Data Architecture
  • Principles of Good Data Architecture
    • Principle 1: Choose Common Components Wisely
    • Principle 2: Plan for Failure
    • Principle 3: Architect for Scalability
    • Principle 4: Architecture Is Leadership
    • Principle 5: Always Be Architecting
    • Principle 6: Build Loosely Coupled Systems
    • Principle 7: Make Reversible Decisions
    • Principle 8: Prioritize Security
      • Hardened-perimeter and zero-trust security models
      • The shared responsibility model
      • Data engineers as security engineers
    • Principle 9: Embrace FinOps
  • Major Architecture Concepts
    • Domains and Services
    • Distributed Systems, Scalability, and Designing for Failure
    • Tight Versus Loose Coupling: Tiers, Monoliths, and Microservices
      • Architecture tiers
        • Single tier
        • Multitier
      • Monoliths
      • Microservices
      • Considerations for data architecture
    • User Access: Single Versus Multitenant
    • Event-Driven Architecture
    • Brownfield Versus Greenfield Projects
      • Brownfield projects
      • Greenfield projects
  • Examples and Types of Data Architecture
    • Data Warehouse
      • The cloud data warehouse
      • Data marts
    • Data Lake
    • Convergence, Next-Generation Data Lakes, and the Data Platform
    • Modern Data Stack
    • Lambda Architecture
    • Kappa Architecture
    • The Dataflow Model and Unified Batch and Streaming
    • Architecture for IoT
      • Devices
      • Interfacing with devices
        • IoT gateway
        • Ingestion
        • Storage
        • Serving
      • Scratching the surface of the IoT
    • Data Mesh
    • Other Data Architecture Examples
  • Whos Involved with Designing a Data Architecture?
  • Conclusion
  • Additional Resources
• 4. Choosing Technologies Across the Data Engineering Lifecycle
  • Team Size and Capabilities
  • Speed to Market
  • Interoperability
  • Cost Optimization and Business Value
    • Total Cost of Ownership
    • Total Opportunity Cost of Ownership
    • FinOps
  • Today Versus the Future: Immutable Versus Transitory Technologies
    • Our Advice
  • Location
    • On Premises
    • Cloud
    • Hybrid Cloud
    • Multicloud
    • Decentralized: Blockchain and the Edge
    • Our Advice
      • Choose technologies for the present, but look toward the future
    • Cloud Repatriation Arguments
      • You are not Dropbox, nor are you Cloudflare
  • Build Versus Buy
    • Open Source Software
      • Community-managed OSS
      • Commercial OSS
    • Proprietary Walled Gardens
      • Independent offerings
      • Cloud platform proprietary service offerings
    • Our Advice
  • Monolith Versus Modular
    • Monolith
    • Modularity
    • The Distributed Monolith Pattern
    • Our Advice
  • Serverless Versus Servers
    • Serverless
    • Containers
    • How to Evaluate Server Versus Serverless
    • Our Advice
  • Optimization, Performance, and the Benchmark Wars
    • Big Data...for the 1990s
    • Nonsensical Cost Comparisons
    • Asymmetric Optimization
    • Caveat Emptor
  • Undercurrents and Their Impacts on Choosing Technologies
    • Data Management
    • DataOps
    • Data Architecture
    • Orchestration Example: Airflow
    • Software Engineering
  • Conclusion
  • Additional Resources
• II. The Data Engineering Lifecycle in Depth
• 5. Data Generation in Source Systems
  • Sources of Data: How Is Data Created?
  • Source Systems: Main Ideas
    • Files and Unstructured Data
    • APIs
    • Application Databases (OLTP Systems)
      • ACID
      • Atomic transactions
      • OLTP and analytics
    • Online Analytical Processing System
    • Change Data Capture
    • Logs
      • Log encoding
      • Log resolution
      • Log latency: Batch or real time
    • Database Logs
    • CRUD
    • Insert-Only
    • Messages and Streams
    • Types of Time
  • Source System Practical Details
    • Databases
      • Major considerations for understanding database technologies
      • Relational databases
      • Nonrelational databases: NoSQL
        • Key-value stores
        • Document stores
        • Wide-column
        • Graph databases
        • Search
        • Time series
    • APIs
      • REST
      • GraphQL
      • Webhooks
      • RPC and gRPC
    • Data Sharing
    • Third-Party Data Sources
    • Message Queues and Event-Streaming Platforms
      • Message queues
        • Message ordering and delivery
        • Delivery frequency
        • Scalability
      • Event-streaming platforms
        • Topics
        • Stream partitions
        • Fault tolerance and resilience
  • Whom Youll Work With
  • Undercurrents and Their Impact on Source Systems
    • Security
    • Data Management
    • DataOps
    • Data Architecture
    • Orchestration
    • Software Engineering
  • Conclusion
  • Additional Resources
• 6. Storage
  • Raw Ingredients of Data Storage
    • Magnetic Disk Drive
    • Solid-State Drive
    • Random Access Memory
    • Networking and CPU
    • Serialization
    • Compression
    • Caching
  • Data Storage Systems
    • Single Machine Versus Distributed Storage
    • Eventual Versus Strong Consistency
    • File Storage
      • Local disk storage
      • Network-attached storage
      • Cloud filesystem services
    • Block Storage
      • Block storage applications
      • RAID
      • Storage area network
      • Cloud virtualized block storage
      • Local instance volumes
    • Object Storage
      • Object stores for data engineering applications
      • Object lookup
      • Object consistency and versioning
      • Storage classes and tiers
      • Object storebacked filesystems
    • Cache and Memory-Based Storage Systems
      • Example: Memcached and lightweight object caching
      • Example: Redis, memory caching with optional persistence
    • The Hadoop Distributed File System
      • Hadoop is dead. Long live Hadoop!
    • Streaming Storage
    • Indexes, Partitioning, and Clustering
      • The evolution from rows to columns
      • From indexes to partitions and clustering
      • Example: Snowflake micro-partitioning
  • Data Engineering Storage Abstractions
    • The Data Warehouse
    • The Data Lake
    • The Data Lakehouse
    • Data Platforms
    • Stream-to-Batch Storage Architecture
  • Big Ideas and Trends in Storage
    • Data Catalog
      • Catalog application integration
      • Automated scanning
      • Data portal and social layer
      • Data catalog use cases
    • Data Sharing
    • Schema
    • Separation of Compute from Storage
      • Colocation of compute and storage
      • Separation of compute and storage
        • Ephemerality and scalability
        • Data durability and availability
      • Hybrid separation and colocation
        • Example: AWS EMR with S3 and HDFS
        • Example: Apache Spark
        • Example: Apache Druid
        • Example: Hybrid object storage
      • Zero-copy cloning
    • Data Storage Lifecycle and Data Retention
      • Hot, warm, and cold data
        • Hot data
        • Warm data
        • Cold data
        • Storage tier considerations
      • Data retention
        • Value
        • Time
        • Compliance
        • Cost
    • Single-Tenant Versus Multitenant Storage
  • Whom Youll Work With
  • Undercurrents
    • Security
    • Data Management
      • Data catalogs and metadata management
      • Data versioning in object storage
      • Privacy
    • DataOps
      • Systems monitoring
      • Observing and monitoring data
    • Data Architecture
    • Orchestration
    • Software Engineering
  • Conclusion
  • Additional Resources
• 7. Ingestion
  • What Is Data Ingestion?
  • Key Engineering Considerations for the Ingestion Phase
    • Bounded Versus Unbounded Data
    • Frequency
    • Synchronous Versus Asynchronous Ingestion
    • Serialization and Deserialization
    • Throughput and Scalability
    • Reliability and Durability
    • Payload
      • Kind
      • Shape
      • Size
      • Schema and data types
        • Detecting and handling upstream and downstream schema changes
        • Schema registries
      • Metadata
    • Push Versus Pull Versus Poll Patterns
  • Batch Ingestion Considerations
    • Snapshot or Differential Extraction
    • File-Based Export and Ingestion
    • ETL Versus ELT
    • Inserts, Updates, and Batch Size
    • Data Migration
  • Message and Stream Ingestion Considerations
    • Schema Evolution
    • Late-Arriving Data
    • Ordering and Multiple Delivery
    • Replay
    • Time to Live
    • Message Size
    • Error Handling and Dead-Letter Queues
    • Consumer Pull and Push
    • Location
  • Ways to Ingest Data
    • Direct Database Connection
    • Change Data Capture
      • Batch-oriented CDC
      • Continuous CDC
      • CDC and database replication
      • CDC considerations
    • APIs
    • Message Queues and Event-Streaming Platforms
    • Managed Data Connectors
    • Moving Data with Object Storage
    • EDI
    • Databases and File Export
    • Practical Issues with Common File Formats
    • Shell
    • SSH
    • SFTP and SCP
    • Webhooks
    • Web Interface
    • Web Scraping
    • Transfer Appliances for Data Migration
    • Data Sharing
  • Whom Youll Work With
    • Upstream Stakeholders
    • Downstream Stakeholders
  • Undercurrents
    • Security
    • Data Management
      • Schema changes
      • Data ethics, privacy, and compliance
    • DataOps
      • Data-quality tests
    • Orchestration
    • Software Engineering
  • Conclusion
  • Additional Resources
• 8. Queries, Modeling, and Transformation
  • Queries
    • What Is a Query?
      • Data definition language
      • Data manipulation language
      • Data control language
      • Transaction control language
    • The Life of a Query
    • The Query Optimizer
    • Improving Query Performance
      • Optimize your join strategy and schema
      • Use the explain plan and understand your querys performance
      • Avoid full table scans
      • Know how your database handles commits
      • Vacuum dead records
      • Leverage cached query results
    • Queries on Streaming Data
      • Basic query patterns on streams
        • The fast-follower approach
        • The Kappa architecture
      • Windows, triggers, emitted statistics, and late-arriving data
        • Session window
        • Fixed-time windows
        • Sliding windows
        • Watermarks
      • Combining streams with other data
        • Conventional table joins
        • Enrichment
        • Stream-to-stream joining
  • Data Modeling
    • What Is a Data Model?
    • Conceptual, Logical, and Physical Data Models
    • Normalization
    • Techniques for Modeling Batch Analytical Data
      • Inmon
      • Kimball
        • Fact tables
        • Dimension tables
        • Star schema
      • Data Vault
        • Hubs
        • Links
        • Satellites
      • Wide denormalized tables
    • Modeling Streaming Data
  • Transformations
    • Batch Transformations
      • Distributed joins
        • Broadcast join
        • Shuffle hash join
      • ETL, ELT, and data pipelines
      • SQL and code-based transformation tools
        • SQL is declarative...but it can still build complex data workflows
        • Example: When to avoid SQL for batch transformations in Spark
        • Example: Optimizing Spark and other processing frameworks
      • Update patterns
        • Truncate and reload
        • Insert only
        • Delete
        • Upsert/merge
      • Schema updates
      • Data wrangling
      • Example: Data transformation in Spark
      • Business logic and derived data
      • MapReduce
      • After MapReduce
    • Materialized Views, Federation, and Query Virtualization
      • Views
      • Materialized views
      • Composable materialized views
      • Federated queries
      • Data virtualization
    • Streaming Transformations and Processing
      • Basics
      • Transformations and queries are a continuum
      • Streaming DAGs
      • Micro-batch versus true streaming
  • Whom Youll Work With
    • Upstream Stakeholders
    • Downstream Stakeholders
  • Undercurrents
    • Security
    • Data Management
    • DataOps
    • Data Architecture
    • Orchestration
    • Software Engineering
  • Conclusion
  • Additional Resources
• 9. Serving Data for Analytics, Machine Learning, and Reverse ETL
  • General Considerations for Serving Data
    • Trust
    • Whats the Use Case, and Whos the User?
    • Data Products
    • Self-Service or Not?
    • Data Definitions and Logic
    • Data Mesh
  • Analytics
    • Business Analytics
    • Operational Analytics
    • Embedded Analytics
  • Machine Learning
  • What a Data Engineer Should Know About ML
  • Ways to Serve Data for Analytics and ML
    • File Exchange
    • Databases
    • Streaming Systems
    • Query Federation
    • Data Sharing
    • Semantic and Metrics Layers
    • Serving Data in Notebooks
  • Reverse ETL
  • Whom Youll Work With
  • Undercurrents
    • Security
    • Data Management
    • DataOps
    • Data Architecture
    • Orchestration
    • Software Engineering
  • Conclusion
  • Additional Resources
• III. Security, Privacy, and the Future of Data Engineering
• 10. Security and Privacy
  • People
    • The Power of Negative Thinking
    • Always Be Paranoid
  • Processes
    • Security Theater Versus Security Habit
    • Active Security
    • The Principle of Least Privilege
    • Shared Responsibility in the Cloud
    • Always Back Up Your Data
    • An Example Security Policy
  • Technology
    • Patch and Update Systems
    • Encryption
      • Encryption at rest
      • Encryption over the wire
    • Logging, Monitoring, and Alerting
    • Network Access
    • Security for Low-Level Data Engineering
      • Internal security research
  • Conclusion
  • Additional Resources
• 11. The Future of Data Engineering
  • The Data Engineering Lifecycle Isnt 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
    • The Live Data Stack
    • Streaming Pipelines and Real-Time Analytical Databases
    • The Fusion of Data with Applications
    • The Tight Feedback Between Applications and ML
    • Dark Matter Data and the Rise of...Spreadsheets?!
  • Conclusion
• A. Serialization and Compression Technical Details
  • Serialization Formats
    • Row-Based Serialization
      • CSV: The nonstandard standard
      • XML
      • JSON and JSONL
      • Avro
    • Columnar Serialization
      • Parquet
      • ORC
      • Apache Arrow or in-memory serialization
    • Hybrid Serialization
      • Hudi
      • Iceberg
  • Database Storage Engines
  • Compression: gzip, bzip2, Snappy, Etc.
• B. Cloud Networking
  • Cloud Network Topology
    • Data Egress Charges
    • Availability Zones
    • Regions
    • GCP-Specific Networking and Multiregional Redundancy
    • Direct Network Connections to the Clouds
  • CDNs
  • The Future of Data Egress Fees
• Index

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