Data modeling fundamentals
Model the domain's real entities, relationships, and invariants.
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Data architecture for backend engineers
Model, store, and evolve data so it stays correct as the system grows.
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Normalization and denormalization
Trade write simplicity against read speed on purpose.
Transactions and isolation levels
What each isolation level prevents and the anomalies it still permits.
Event sourcing basics
Store state as an append-only log of the decisions that occurred.
Change data capture and data pipelines
Move data between systems from the log instead of from dual writes.
Data retention and archival
Decide what to keep, for how long, and where it lives afterward.
Schema evolution and migrations
Change schemas without downtime, data loss, or a big-bang cutover.
Read and write models: CQRS
Model the read and write sides separately when their needs diverge.
Consistency patterns
Strong, eventual, and causal consistency, and what each one costs.
Data quality and validation
Enforce invariants at the boundary so bad data never lands.