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275 questions, 991 terms and 600 topics in 30 areas.
60 results for “Data Vault Modelling”
Hub and Satellite
Separating stable business keys from their changing attributes and from their relationships, so each can be loaded independently and kept forever.
Access Pattern Driven Modelling
Designing the data model from the queries the application must serve, rather than from an abstract normalised representation of the entities.
Data-Flow Diagram
A diagram of how data moves between processes, stores and external entities, with trust boundaries drawn on it.
Denormalisation
Deliberately duplicating data across records to make reads cheap, accepting the write-time cost of keeping copies in step.
SQL vs NoSQL
A choice driven by access patterns, consistency requirements and query flexibility — not by data volume, which is the reason usually given.
STRIDE
A mnemonic for six threat categories — spoofing, tampering, repudiation, information disclosure, denial of service, elevation of privilege — walked across each component and data flow.
Star Schema
A dimensional model with one central fact table of measurements surrounded by denormalised dimension tables describing them.
Capital One's Data Centre Exit
A major US bank closed all eight of its data centres and moved fully to public cloud, treating governance automation as the enabling technology rather than a constraint.
Change Data Capture
Publishing a stream of a database's row-level changes by reading its replication log, without modifying the application that owns it.
Control Plane and Data Plane
The separation between the machinery that makes changes to a system and the machinery that serves its traffic.
Cross-Zone Data Transfer
Charges incurred when data moves between availability zones within a region — invisible on architecture diagrams and a recurring surprise on cloud bills.
Data Catalog
A searchable inventory of datasets with their schema, owner, meaning, freshness, quality and classification.
Data Contract
An explicit, versioned, enforced agreement between a data producer and its consumers about schema, semantics, quality and change policy.
Data Discovery
Automatically scanning stores to find where sensitive data actually resides, as distinct from where the documentation says it should.
Data Lakehouse
A pattern that puts warehouse-style transactions, schema and governance on top of cheap open-format object storage.
Data Lineage
A record of where each dataset came from, what transformed it, and what depends on it — traced at table and ideally column level.
Data Lineage View
A view showing how data moves and is transformed through a system, independent of the components that do the moving.
Data Mesh
An organisational approach that gives domain teams ownership of their analytical data as a product, with a self-serve platform and federated governance.
Data Minimisation
Collecting and retaining only what a stated purpose requires, which reduces both regulatory exposure and breach impact at the same time.
A finance report double-counts revenue after a new fact table is added. What is the likely modelling error?
The likely error: a fan out join between fact tables at different grains The classic mechanism. You have an order lines fact at line grain and a shipments fact
A transformation project has grown to 600 models with chains twelve deep. A change at the base has an unknowable blast radius. What do you do?
Treat it as a software architecture problem, because it is one Six hundred models with twelve deep chains is a codebase with no module boundaries. The remedies
Discord stores trillions of messages. What is their partition key, and what problem does the second half of it solve?
The key (channel id, bucket) — where bucket is a fixed time window. What each half does channel id matches the read pattern. Clients read messages within a chan
A CDO proposes moving to a data mesh because the central data team is a bottleneck with a nine-month backlog. How do you assess the proposal?
Agree with the diagnosis, examine the prescription The bottleneck is real and it is structural rather than a matter of capacity. A central team receives data fr
A GDPR erasure request arrives for a customer. Where does their data actually live, and what makes this expensive to retrofit?
Where the data lives Longer than people expect, and enumerating it is most of the work: Primary database · read replicas · caches · search indexes · analytical
A business sponsor asks for a real-time data platform because "the competition has one". Reporting is currently a nightly batch that lands at 06:00 and nobody has complained. How do you handle this?
Do not answer the technology question "Real time platform" is a solution, and it has arrived without a problem attached. Answering it directly leads either to a
A new platform must serve a public partner API, three internal front-ends with different data needs, and high-volume service-to-service traffic. Choose the API styles and defend the choice.
Resist "pick one" These are three different problems with three different consumers. Standardising on one style optimises for architectural tidiness at the expe
A query that ran in 50ms for two years now takes 90 seconds. Nothing was deployed and the data volume grew normally. What happened?
The most likely cause: a plan flip The optimiser's choice is a function of estimated row counts. As the data grows or its distribution shifts, an estimate cross
A regulated client requires that no traffic between their data centre and your SaaS platform traverses the public internet. Design the connectivity and justify the cost.
Two distinct requirements hiding in one sentence Traffic must not traverse the public internet — a routing requirement. The client must be able to demonstrate i
A regulator asks whether customer data is encrypted. The team says yes, disks are encrypted. Is that a sufficient answer?
What disk encryption actually protects against Someone obtaining the physical medium or a raw storage snapshot. In a cloud context that means a provider employe
A team proposes storing the customer's address on every order row "so order history is accurate". Is that denormalisation or a modelling error?
The distinction that matters It is neither, quite — it is a temporal modelling requirement being solved by accident. Denormalisation duplicates a fact for perfo
Choose storage for four workloads: a Postgres data directory, user-uploaded images, a shared build cache, and seven years of audit records.
Postgres data directory — block storage It needs low latency random reads and writes and a filesystem, and it attaches to one instance. That is precisely block
Data Vault Modelling
Hubs, links and satellites, and the auditability and load parallelism they buy.
Data Warehousing
Dimensional modelling, star schemas and analytical workloads.
Dimensional Modelling
Facts, dimensions, grain, and the star schema's continued relevance.
Relational Modelling
Normalisation, keys, constraints and the invariants a schema enforces.
Architecture Cost Modelling
Pricing a design before building it, at expected and at ten times volume.
Capacity Modelling
Arithmetic before load tests, and headroom for failure as well as peak.
Change Data Capture
Turning a database's replication log into a stream, and its coupling risk.
Client Caching & Data Layer
Stale-while-revalidate, invalidation and optimistic updates on the client.
Data Access Models
Role, attribute and purpose-based access over analytical data, and how they compose.
Data Architecture
General material on structuring, storing and governing data.
Data Catalog
Discovery, ownership and technical metadata, and why catalogues go stale.
Data Classification
Knowing which fields are regulated, because every control depends on it.
Data Contracts
Producers committing to schema, semantics and freshness, and breaking builds when they do not.
Data Governance
Ownership, lineage, quality, catalogues and who may see what.
Data Governance & Semantics
General material on ownership, meaning, quality and control of data at enterprise scale.
Data Lakes & Lakehouses
Open formats on object storage with transactional metadata on top.
Data Lifecycle & Retention
How long data is kept, where it ages to, and how it is actually deleted.
Data Mesh
Domain ownership, data as a product, self-serve platform, and federated governance.
Data Migration Strategies
Backfill, dual-write, reconciliation and verification.