Event Sourcing
Storing the change log as the system of record, and what that costs forever.
5 to work through
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advanced
A brokerage proposes event sourcing for its account ledger. What requirements would genuinely justify it, and when is conventional state plus an audit trail the safer choice?
2 min answer -
advanced
A neobank stores every account change as an immutable fact rather than a mutable balance. Which needs justify this, how are balances materialised for fast reads, and what happens when a projection is found to be wrong?
3 min answer -
advanced Multiple choice
A system is event-sourced. A customer exercises their right to erasure. The event log is immutable. How do you comply?
2 min answer -
advanced
A team wants event sourcing for a new order service, citing audit requirements. What do you recommend?
2 min answer -
advanced Multiple choice
An organisation wants event sourcing for its order platform because it is considered scalable. What business or audit requirements would actually justify it, and when is CRUD plus an audit log safer?
2 min answer
4 terms in this topic
Event Sourcing in Practice
Storing state as an immutable sequence of events and deriving current state by replay — powerful where history is the domain, expensive everywhere else.
toolEvent Store
An append-only store of domain events organised into per-entity streams, serving as the system of record rather than as a log beside it.
patternEvent Upcasting
Transforming an old event's stored form into the current shape as it is read, so historical events remain replayable after the schema changes.
patternSnapshotting
Periodically storing an aggregate's computed state so it can be loaded without replaying its entire event history.
Neighbouring topics
Data Architecture
General material on structuring, storing and governing data.
Relational Modelling
Normalisation, keys, constraints and the invariants a schema enforces.
NoSQL Stores
Key-value, document, wide-column and graph — what each buys and forbids.
Indexing
Designing indexes per query shape, and paying for them on every write.
Query Optimisation
Reading a plan, fixing statistics, and finding the real bottleneck.
Transactions & Isolation
ACID, isolation levels, and the anomalies each level permits.
Replication
Primaries, replicas, lag, and synchronous versus asynchronous durability.
Partitioning & Sharding
Splitting data across machines, and the one-way door of a partition key.
Caching Strategies
Cache-aside, read-through, write-through and where each belongs.
Cache Invalidation
Stampedes, penetration, staleness windows and versioned keys.
CQRS
Separating the write model from the read models that serve queries.
Change Data Capture
Turning a database's replication log into a stream, and its coupling risk.
Data Warehousing
Dimensional modelling, star schemas and analytical workloads.
Data Lakes & Lakehouses
Open formats on object storage with transactional metadata on top.
ETL & ELT
Where transformation happens, and how much raw history you keep.
Streaming Data
Windowing, watermarks, late arrivals and exactly-once semantics.
Data Governance
Ownership, lineage, quality, catalogues and who may see what.
Data Lifecycle & Retention
How long data is kept, where it ages to, and how it is actually deleted.
Polyglot Persistence
Choosing a store per workload, and the operational cost of variety.