Data & Storage Architecture
1 article published
The database is often the hardest component to change after launch. This pillar gives you the tools to make informed choices about storage engines, replication, and data modelling before you are locked in.
Reading Path
Data Modeling Fundamentals
How data shape drives every downstream storage and query decision.
Coming soonSQL vs NoSQL Tradeoffs
Stop treating this as a default choice. Learn when each model wins.
Coming soonQuery Patterns & Access Design
Designing schemas around how data is read, not just how it is stored.
Coming soonReplication Models
Sync vs async replication, leader election, and failure scenarios.
Coming soonSharding Strategies
Range, hash, directory — and the complexity that comes with each.
Coming soonCaching Layers
Read-through, write-through, cache invalidation, and when caching hurts.
Coming soonData Consistency & Integrity
Transactions, constraints, and the real cost of eventual consistency.
Coming soonData Lakes vs Warehouses
Analytics infrastructure decisions and their cost implications.
Coming soonIndexing Strategies (B-Tree, LSM — Conceptual)
Why index structure determines read vs write performance tradeoffs.
Coming soonStorage Engines (Conceptual Overview)
What sits beneath your database and why it matters for your workload.
Coming soonSchema Design & Migrations
Evolving schemas without downtime — and the traps to avoid.
Coming soonBackups & Disaster Recovery
RPO, RTO, and how to actually validate that your backups work.
Coming soonStorage Tiering (Hot vs Cold Data)
Moving data to cheaper storage without losing access when you need it.
Coming soonData Pipelines (Batch vs Streaming)
When to process data as it arrives vs in scheduled windows.
Coming soon