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275 questions, 991 terms and 600 topics in 30 areas.
60 results for “Self-Service vs Governed”
Governed Self-Service
Letting anyone analyse anything while constraining what may be treated as authoritative, which resolves the apparent conflict between speed and consistency.
Managed vs Self-Managed
Trading control, portability and unit cost against the operational burden of running the thing yourself.
Provisioning Self-Service
Teams obtaining infrastructure without a ticket, within bounds that make the request safe by construction rather than by review.
Operational Burden Assessment
Quantifying the ongoing engineering effort a self-managed component requires, so it can be compared honestly with a managed service's price.
Managed Service Substitution
Replacing a self-managed component with a managed equivalent during migration, without changing the application's architecture.
Developer Portal
The self-service surface where consumers discover APIs, read documentation, obtain credentials and test calls — the main determinant of adoption.
JSON Web Token
A signed, self-contained token carrying claims, which a service can validate locally without calling the issuer.
Active-Active vs Active-Passive
Whether all regions serve traffic simultaneously, or one serves while another waits to take over — a choice about which failure mode you would rather have.
Airbnb's Service-Oriented Migration
Airbnb decomposed a large Rails monolith by first extracting a unified data-access layer, so that services were built on owned data rather than on shared database tables.
Build vs Buy
The choice between developing a capability in-house and acquiring it, decided on differentiation and total cost rather than on feature lists.
Containment vs Eradication
Stopping an attacker's ongoing access versus removing their foothold entirely — sequential phases with different urgency and different risks of doing them wrong.
Cost vs Reliability Trade-off
The non-linear relationship between availability and spend, which makes each additional nine roughly an order of magnitude more expensive.
Cross-Service Debugging
Investigating a failure that spans multiple services by moving between traces, logs, metrics and profiles along a single correlated request.
Database per Service
Each service owning its own datastore, with no other service reading or writing it directly.
Delivery vs Maintainability
Choosing where to take deliberate shortcuts, based on which kinds of debt are cheap to repay and which compound.
Durability vs Availability
Two different storage guarantees — whether data survives, and whether it can be reached right now — routinely conflated because both are quoted in nines.
ETL vs ELT
Whether data is transformed before loading into the target or after it, which decides where the compute happens and how much raw history you keep.
Event Notification vs Event-Carried State
Whether an event carries only the fact that something happened, or also the data a consumer needs to act on it.
Fail-Fast vs Fail-Safe
Whether a component should stop immediately on detecting a problem, or continue in a degraded but safe mode — a choice that depends entirely on which outcome is worse.
A team wants to self-manage Kafka on Kubernetes to save money against the managed service. Evaluate.
The comparison they have probably made Instance cost versus managed service list price. That comparison usually favours self managing by a wide margin, and it o
A service has widely varying request costs — most complete in 10 ms, some take 5 s. Under round robin some instances are overwhelmed while others idle. What do you change?
Why round robin fails here It distributes by count , not by cost or by whether a backend is coping. With uniform requests that is fine. With a 500× cost spread,
Your platform team is now a ticket queue — every environment, every permission, every new service goes through them. How do you get out of it?
Categorise the queue before automating anything Take a month of tickets and group them. The distribution is always uneven, and two or three categories are usual
A 43-second network partition caused GitHub over 24 hours of degraded service in 2018. How does a 43-second event become a day-long incident?
The case, as publicly reported On 21 October 2018, routine maintenance replacing failing optical equipment caused a 43 second loss of connectivity between GitHu
A card payment authorisation service runs active-active across two regions. A network partition splits them. Do you keep accepting authorisations, and what breaks either way?
What the interviewer is testing Whether you can apply CAP to a domain where the cost of each choice is concrete, and whether you recognise that "it depends" has
A downstream service slows from 50 ms to 3 s. Within two minutes every service in the request path is down, including ones that do not call it. Explain the mechanism and how you would have prevented it.
What the interviewer is testing Whether you understand that most outages are amplification, not failure — and whether you can name the specific mechanism rather
A downstream team needs to react to order changes. The order service can publish events, or they can consume CDC from its database. Which, and why?
The recommendation: published events, with CDC as the mechanism if needed The distinction that matters is what the consumer becomes coupled to . CDC consumed di
A global SaaS product has a 200 ms p95 API budget. Users in Sydney see 900 ms against a single US-East deployment. The application team says the service responds in 40 ms. Who is right and what do you do?
Both are right, and that is the point The service does respond in 40 ms. The other 860 ms is network — and no amount of application profiling will show it, whic
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 platform team proposes adopting a service mesh for 40 services. Make the case for and against, then decide.
What it genuinely provides mTLS everywhere, with automated certificate rotation. This is usually the deciding factor. Doing mTLS by hand means short lived certi
A service autoscales on CPU. During incidents it never scales out, even as latency triples. Why, and what would you scale on instead?
Why CPU never triggers The service is latency bound, not compute bound. Its threads are waiting — on a database, a downstream API, a lock, or a connection pool
A service becomes unresponsive during an incident in a dependency it barely uses. Investigation finds the client had no read timeout. Explain the full mechanism.
The mechanism, step by step 1. The dependency slows. It has not failed — it accepts connections and eventually responds, just very slowly. This is the important
A service calls four dependencies. How do you size its thread pools, and why is the total often smaller than people expect?
The sizing rule Little's Law, per dependency: concurrency = throughput to that dependency × its latency. Dependency Calls/s Latency Concurrency Pool Auth 500 10
A service intermittently times out connecting to a managed database. The database is healthy, nothing changed, and failures correlate with traffic volume. Where do you look?
The likely cause NAT gateway port exhaustion. Source NAT maps each outbound connection to a source port on the gateway's address. The number of simultaneous con
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 service's p99 latency has tripled over three months with no single obvious change. How do you investigate?
Establish the shape before touching anything Is it everything or something? Break the metric down by endpoint, tenant, region, instance and version. A tripling
A team is launching a new service and asks what its SLO should be. How do you help them decide, and why is "99.99%" usually the wrong first answer?
What the interviewer is testing Whether you treat reliability as a cost benefit decision with a budget, or as a virtue to maximise. Why not four nines It costs
A team proposes CQRS with event sourcing for a new service. How do you evaluate the proposal?
Separate the two, because they are independent decisions CQRS is separating the write model from the read model. Event sourcing is storing state as a sequence o
A team proposes exposing their service's database change stream via CDC so other teams can consume it, avoiding the work of building an event API. What is your assessment?
Name what is actually being proposed The proposal is to publish the service's internal schema as its integration contract. CDC does not emit domain events; it e
A team wants event sourcing for a new order service, citing audit requirements. What do you recommend?
The recommendation: probably an audit log, not event sourcing If the requirement is audit , event sourcing is a very expensive way to obtain it. An append only
After a five-minute network outage at your ingest tier, 200,000 devices reconnect simultaneously and the authentication service collapses. How do you fix this?
The mechanism Every device detected the disconnection at roughly the same moment and retried immediately. Reconnection is the most expensive operation in the fl
After moving a service behind a VPN to an on-premises system, small API calls succeed and large responses hang indefinitely. Walk through the diagnosis.
What the symptom tells you Size dependent failure with a working handshake is a very specific signature. The connection establishes, so routing, firewall rules,
An order service must notify inventory, billing, shipping and analytics when an order is placed. Synchronous calls or events? Justify your choice per consumer.
What the interviewer is testing Whether you apply the decision per interaction rather than adopting one style globally. The framing that matters Synchronous cal
An order service publishes events consumed by six teams. Every consumer immediately calls back for order details. What is wrong and what do you change?
The failure Thin notification events ("order 123 changed") produce a callback stampede : every event triggers six synchronous calls back to the producer, and th
An order spans payment, inventory and shipping, each in its own service with its own database. How do you keep them consistent?
Rule out the distributed transaction Two phase commit gives atomicity and isolation across services, and its costs make it unusable at scale: locks held across
Design rate limiting for a multi-tenant API where a single customer's traffic spike currently degrades service for everyone.
Name the problem precisely This is the noisy neighbour problem. Rate limiting is one control for it, and on its own it is incomplete — a customer within their l
Self-Service vs Governed
Letting analysts move fast without four teams reporting four different revenues.
Managed vs Self-Managed
Trading control and unit cost against operational attention.
Self-Service Provisioning
Teams getting infrastructure without a ticket, and the guardrails that make that safe.
Build vs Buy
Differentiation versus table stakes, priced over five years.
Build vs Buy
Differentiation, five-year TCO, and the exit cost of each option.
Centralised vs Distributed
Shared platform leverage against team autonomy.
Change Advisory vs Automated Gates
Replacing a weekly board with evidence a machine produces on every change.
Change Management vs CD
Reconciling CAB-era controls with continuous delivery without pretending either away.
Control Design vs Operation
A control that is well designed and never runs fails exactly like one that is absent.
Cost vs Reliability
Each nine costing an order of magnitude, and pricing the failure instead.
Delivery vs Maintainability
Fast in the cheap places, careful in the expensive ones.
Erasure vs Immutability
Deletion obligations against event logs, backups and ledgers designed never to forget.
Functional vs Non-Functional
Behaviour versus quality of behaviour, and why only the second constrains structure.
Guardrails vs Gates
Preventing a class of mistake automatically versus stopping to ask a human.