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: Building a CDC Pipeline
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- Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale, Gwen Shapira, Todd Palino, Rajini Sivaram, Krit Petty, 2021 (O'Reilly Media) - A comprehensive guide to Apache Kafka, explaining its architecture, stream processing, and its role as a message bus for high-throughput data ingestion pipelines, crucial for the transport layer.
- The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, Ralph Kimball, Margy Ross, 2013 (Wiley) - A foundational book on data warehousing methodologies, offering context on ETL strategies, data integration, and maintaining data consistency in analytical systems, relevant to CDC goals.
- Snowflake Documentation: MERGE Statement and Working with Semi-Structured Data, Snowflake Inc., 2024 (Snowflake Inc.) - Official Snowflake documentation detailing the
MERGESQL statement for idempotent upserts and soft deletes, and strategies for handling schema drift using semi-structured data types likeVARIANTin data warehouses.
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