Data management covers the storage, retrieval, transformation, and movement of data at scale. The explosion of database paradigms, caching systems, and streaming platforms over the past decade means engineers must understand a broad landscape to make effective choices for their workloads.
Overview
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Database Paradigms
Relational SQL
Document NoSQL
Key-Value
Wide Column
Graph
Time-Series
Vector
Consistency and Transactions
ACID Properties
BASE Properties
Transaction Isolation Levels
Distributed Transactions
Two-Phase Commit
Data Modeling
ER Modeling
Normalization
Denormalization
Schema Design
Polyglot Persistence
Caching
In-Process Cache
Distributed Cache
Cache Aside
Write-Through
Write-Behind
Read-Through
Replication and Partitioning
Leader-Follower
Multi-Leader
Leaderless
Range Partitioning
Hash Partitioning
Data Pipelines
ETL vs ELT
Batch Processing
Stream Processing
Change Data Capture
Data Lakehouse
Topics in This Section
| File | Topic | Key Concepts |
|---|---|---|
| 01_database_paradigms.md | Database Paradigms | SQL, NoSQL types, vector DBs |
| 02_consistency_transactions.md | Consistency & Transactions | ACID, BASE, isolation levels |
| 03_data_modeling.md | Data Modeling | ER, normalization, schema design |
| 04_caching_strategies.md | Caching | Cache patterns, eviction, invalidation |
| 05_replication_partitioning.md | Replication & Partitioning | Replication modes, sharding strategies |
| 06_data_pipelines.md | Data Pipelines | ETL/ELT, CDC, streaming, lakehouse |