Database Concepts — From Fundamentals to Expert

Master the bottom of the pyramid first, then work your way up.

This site is a progressive learning guide covering database engineering from first principles to distributed systems design, with concrete examples from cloud-native databases.

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Learning Pyramid

graph BT
    A["🔷 Foundations\n1. SQL vs NoSQL · 2. ACID · 3. Normalization · 4. Indexing"]
    B["📈 Scaling Reads\n5. Replication · 6. Read Replicas · 7. Caching"]
    C["✍️ Scaling Writes\n8. CAP Theorem · 9. Sharding · 10. Isolation Levels"]
    D["⚡ Performance · Distribution\n11. Materialized Views · 12. Consistent Hashing · 13. Eventual Consistency"]
    E["🗄️ Storage Internals\n14. B-Tree vs LSM · 15. WAL · 16. Bloom Filters"]
    F["🔗 Distributed Transactions\n17. Quorum · 18. CDC · 19. 2PC · 20. Saga"]

    A --> B --> C --> D --> E --> F

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Sections

#	Section	Topics
1	Foundations	SQL vs NoSQL, ACID, Normalization, Indexing
2	Scaling Reads	Replication, Read Replicas, Caching
3	Scaling Writes	CAP Theorem, Sharding, Isolation Levels
4	Performance & Distribution	Materialized Views, Consistent Hashing, Eventual Consistency
5	Storage Internals	B-Tree vs LSM, WAL, Bloom Filters
6	Distributed Transactions	Quorum, CDC, 2PC, Saga Pattern

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Databases Covered

Throughout each topic, implementations are compared across:

• PostgreSQL — battle-tested relational
• Google Spanner — globally distributed SQL
• Google BigQuery — serverless analytical
• Amazon DynamoDB — managed NoSQL key-value
• Apache Cassandra — wide-column, high write throughput
• MongoDB — document store
• Redis — in-memory caching layer

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Glossary (Hover Definitions)

Key terms throughout the site use <abbr> tags — hover over underlined dotted terms to see their definitions inline. See the full Glossary.