Introduction
How to read this book
Helpers
Part 1: Data For Foundations
1.
Streaming and Batching Data Processing
1.1.
KB - Kafka for Streaming
1.2.
KB - Lambda and Kappa Design Patterns
1.3.
BP - Optimizing Data Processing
2.
Data Warehouse, Lake, One Big Table
2.1.
KB - Schema Revolutions
2.2.
KB - Partition and Metadata
3.
Dimension Data Modeling
3.1.
KB - Managing Changing Dimension
3.2.
DD - Designing data for Dimension
3.3.
BP - Handling Missing Dimensions
4.
Fact Data Modeling
4.1.
KB - Types of Fact Tables
4.2.
KB - Measures and Metrics
5.
Data Quality Dimensions
5.1.
KB - Data Quality Dimensions
5.2.
BP - Techniques for Data Profiling
Part 2: Data In Advanced
6.
Distributed Systems, Applying Data Area
6.1.
KB - Distributed Data Storage
6.2.
KB - Data Consistency and Availability
6.3.
KB - Scalability and Performance
7.
Data Pipeline Building Spec
7.1.
DD - Designing Data Pipelines
7.2.
BP - ETL vs. ELT
7.3.
KB - Pipeline Orchestration
7.4.
KB - Monitoring and Maintenance
8.
Maintaining Data Warehouse and Pipeline
8.1.
KB - Performance Monitoring
8.2.
BP - Troubleshooting Common Issues
8.3.
BP - Backup and Recovery
8.4.
KB - Upgrading and Scaling
9.
Optimizing Data Warehouse and Pipeline
9.1.
BP - Query Optimization
9.2.
BP - Resource Management
9.3.
BP - Cost Optimization
Part 3: Data For Management
10.
Audit and Govern Data Systems
10.1.
KB - Data Auditing Techniques
10.2.
KB - Compliance and Regulations
10.3.
KB - Data Security Measures
10.4.
KB - Governance Frameworks
11.
Data Impact, Investigation and Visualization
11.1.
KB - Measuring Data Impact
11.2.
BP - Techniques for Data Investigation
11.3.
DD - Data Visualization Principles
11.4.
KB - Tools for Data Visualization
11.5.
KB - Creating Compelling Visuals
Changelog
Copyright
Light
Rust
Coal
Navy
Ayu
Solid Data Foundations (SDF)
BP - Handling Missing Dimensions