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
Columnar Storage Formats and Compression
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- C-Store: A Column-Oriented DBMS, Michael Stonebraker, Daniel J. Abadi, Adam Batourakis, Samuel Madden, Daniel J. Rosenblum, Nesime Tatbul, 2005 VLDB '05: Proceedings of the 31st International Conference on Very Large Data Bases (ACM) DOI: 10.1145/1071133.1071151 - Presents the original architecture and advantages of column-oriented database systems for analytical workloads, contrasting them with traditional row stores.
- The Design and Implementation of Modern Column-Stores, Daniel Abadi, Peter Boncz, Stavros Harizopoulos, Stratos Idreos, Samuel Madden, 2013 Foundations and Trends® in Databases, Vol. 5 (Now Publishers Inc.) DOI: 10.1561/1900000024 - A comprehensive survey of modern columnar database design, covering storage, various compression techniques, vectorized execution, and query optimization strategies.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - Offers broad coverage of data systems, including detailed sections on storage engine designs (row vs. columnar), compression, and query processing, with practical explanations.
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