Data Warehousing
Dimensional modelling, star schemas and analytical workloads.
4 to work through
-
intermediate
An analytics platform separates compute from storage. What does that actually buy architecturally, and what problems does it not solve?
2 min answer -
intermediate
Finance and marketing report different revenue for the same month from the same warehouse. Where do you look and what would have prevented it?
2 min answer -
advanced
A finance report double-counts revenue after a new fact table is added. What is the likely modelling error?
2 min answer -
advanced
A travel platform runs thousands of experiments simultaneously and needs its warehouse to support reliable analysis. What warehouse design decisions prevent cross-experiment contamination and infrastructure artefacts from being read as results?
2 min answer
3 terms in this topic
Data Warehousing
A separate analytical store modelled for questions rather than transactions, physically isolated from the systems that generate the data.
conceptOLTP vs OLAP
Two workload shapes with opposite requirements — many small indexed transactions versus few large scans and aggregations — which is why they belong i…
patternStar Schema
A dimensional model with one central fact table of measurements surrounded by denormalised dimension tables describing them.
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.
Event Sourcing
Storing the change log as the system of record, and what that costs forever.
Change Data Capture
Turning a database's replication log into a stream, and its coupling risk.
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.