Search the practice set
275 questions, 991 terms and 600 topics in 30 areas.
60 results for “Edge Data Consistency”
Read Local Write Global
Replicating read-only state to every edge location while directing writes to a single authoritative region, which is the arrangement most edge data stores actually provide.
Cache Invalidation
The problem of removing or refreshing cached data when the underlying source changes, and the reason caching is harder than it looks.
Compute Tier Placement
Deciding which of on-device, on-premises, metro, regional or central compute a workload belongs to, from its latency, data volume and autonomy requirements.
Dual Write
Writing the same change to both the old and new stores during a migration, and the reconciliation that makes it trustworthy.
SQL vs NoSQL
A choice driven by access patterns, consistency requirements and query flexibility — not by data volume, which is the reason usually given.
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.
Causal Consistency
A model guaranteeing that operations which causally depend on one another are seen in the same order everywhere, while concurrent operations may be seen in any order.
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.
Cold Start at the Edge
The initialisation delay when a request reaches a location with no warm instance, which at the edge occurs far more often because traffic is spread across many locations.
Consistency Level Selection
Choosing the consistency guarantee per operation rather than per system, matching the cost of coordination to the business consequence of staleness.
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 team proposes moving an API to edge functions to reduce latency for international users. What do you check before agreeing?
Where is the data? This is the question that settles most edge proposals. Compute at the edge that calls back to a single region database has moved the compute
Users report seeing stale data intermittently. Replication lag is normally under a second but spikes to minutes twice a day. How do you handle it?
First: find the cause of the spikes Twice a day is a schedule, so look for one. The usual candidates, each with a different fix: A batch job or bulk write on th
A service writes to its database and then publishes an event to Kafka. Sometimes consumers see an event for a record that does not exist, and sometimes a record exists with no event. Why, and how do you fix it?
What the interviewer is testing Recognition of the dual write problem — one of the most common defects in event driven systems and one that testing rarely catch
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 moving personalisation to edge functions to cut latency. When does that work and when does it backfire?
The principle that decides it The edge is near the user and far from your data. Edge compute pays off for work that needs the request but not your state . The m
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
Edge Data Consistency
Replicated read state at hundreds of locations, and writes that still go to one.
Data Quality Dimensions
Completeness, accuracy, timeliness, consistency, validity and uniqueness as testable claims.
Edge, Mobile & IoT
General material on architecture beyond the data centre boundary.
Synthetic Data
Generating data with the shape and edge cases of the real thing, and where it misleads.
CRDTs
Data types that converge without coordination, and the semantics you must accept.
Multi-Region Architecture
Surviving a region, and the data consistency price of doing so.
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.
Consistency Models
Linearizable, sequential, causal, eventual, and the session guarantees between them.
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.