Building Scalable Data Warehouses
Chapter 1: MPP Architecture and Storage Internals
Massively Parallel Processing Fundamentals
Decoupling Compute and Storage
Columnar Storage Formats and Compression
Micro-partitioning and Metadata Management
Hands-on practice: Inspecting Storage Profiles
Chapter 2: Advanced Data Modeling at Scale
Dimensional Modeling Constraints in Big Data
Data Vault 2.0 Implementation Patterns
Handling Semi-Structured Data
Schema Evolution and Versioning
Hands-on practice: Designing a Data Vault
Chapter 3: High-Throughput Ingestion Pipelines
Change Data Capture Architectures
Idempotency in Data Pipelines
Micro-batch vs Streaming Ingestion
Handling Late Arriving Data
Hands-on practice: Building a CDC Pipeline
Chapter 4: Query Optimization and Performance Tuning
Analyzing Query Execution Plans
Partition Pruning and Clustering Keys
Join Strategies: Broadcast vs Shuffle
Materialized Views and Caching Layers
Hands-on practice: Tuning High-Latency Queries
Chapter 5: Governance, Security, and Observability
Role-Based Access Control Hierarchies
Dynamic Data Masking and Tokenization
Row-Level Security Implementation
Resource Monitoring and Cost Control
Hands-on practice: Configuring Security Policies
Hands-on practice: Tuning High-Latency Queries
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- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - This book provides a comprehensive overview of distributed systems principles, including storage, indexing, and query processing, pertinent to understanding the physical execution of queries in scalable data warehouses.
- Understanding the Query Profile, Snowflake Documentation, 2024 (Snowflake) - Provides detailed guidance on interpreting query execution plans, identifying bottlenecks, and understanding operators like TableScan, Join, and Exchange in a modern distributed SQL engine.
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