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126 questions, 454 terms and 400 topics in 20 areas.
60 results for “Data-Flow Diagrams”
Data-Flow Diagram
A diagram of how data moves between processes, stores and external entities, with trust boundaries drawn on it.
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.
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.
Sequence Diagram
A diagram showing the ordered exchange of messages between participants over time, used to make an interaction's control flow and failure points explicit.
Authorization Code Flow with PKCE
The OAuth flow recommended for all client types, in which an authorisation code is exchanged for tokens using a proof key that binds the exchange to the original requester.
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.
Credit-Based Flow Control
A scheme where a receiver grants the sender a budget of bytes or messages it may transmit, replenished as the receiver consumes.
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 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 Residency
A requirement that specific data be stored and sometimes processed only within a defined geography.
Data Retention Policy
A defined rule for how long each class of data is kept, where, and what happens at the end of it.
Hot, Warm and Cold Data
Classifying data by how frequently and how urgently it is accessed, so each tier can be stored on media priced for that access pattern.
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.
Availability Zone
One or more physically separate data centres inside a cloud region, with independent power, cooling and network, connected by low-latency links.
Backfill
Re-running a pipeline over historical periods to populate new data or correct a past error, and the operation that proves whether a pipeline is well designed.
Backpressure
A mechanism by which a component under load tells its callers to slow down, rather than accepting work it cannot complete.
Bloom Filter
A compact probabilistic structure that answers "is this key definitely absent, or possibly present?" — no false negatives, tunable false positives.
Bounded Queue
A queue with a maximum depth, which converts unbounded latency growth into an explicit rejection you can control.
C4 Model
A set of four nested diagram levels — context, container, component, code — that keeps each diagram at one consistent level of abstraction.
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 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 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
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
Maersk rebuilt roughly 4,000 servers and 45,000 PCs in about ten days after NotPetya in 2017, and recovered its directory only because one data centre had been offline during the attack. What does this say about DR design?
The case, as publicly reported In June 2017 the NotPetya malware — destructive rather than financially motivated — propagated through Maersk's network, encrypti
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
You are asked to choose between a cloud data warehouse and a lakehouse for a new analytics platform. How do you decide?
What actually differs The gap has narrowed considerably, so the decision turns on fewer things than the marketing suggests. Format ownership. In a lakehouse, da
You must choose a data store this week. The product team cannot tell you the expected query patterns or the growth rate. What do you do?
What the interviewer is testing How you behave when the information you would like does not exist — which is the normal condition, not the exception. Both "refu
A dashboard has been wrong for three weeks. Nobody knows which upstream produced the table it reads. What is the governance failure, and what fixes it?
The failures, and there are three 1. No lineage. The question "which upstream produced this?" should be answerable in seconds by following a derived dependency
A dashboard query that took 200ms now takes 40 seconds. The table has grown to 200 million rows. Walk me through diagnosis and fix, including what you would not do.
What the interviewer is testing Whether you diagnose with evidence before changing anything, and whether you know the costs of the fixes you propose. Diagnosis,
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 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 multi-tenant SaaS product has outgrown one database. You must shard. How do you choose the partition key, and what makes this decision so expensive to get wrong?
What the interviewer is testing Whether you exhaust cheaper options first, and whether you understand that a shard key is close to irreversible. First: do not s
A product catalogue page does 40,000 reads per second against a database that can serve 5,000. Walk me through the caching design, including what happens at 3 AM when the cache is empty.
What the interviewer is testing Whether you can design a cache including its failure modes, rather than saying "put Redis in front of it". The base design Cache
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 streaming aggregation reports lower totals than the batch job it replaced. Both read the same source. What is likely happening?
The likely cause: late events dropped past the watermark The batch job reads a completed day and sees everything, including records that arrived hours after the
A worker's queue depth grows steadily through the day and never recovers. Adding workers helps for an hour, then it resumes. What is happening?
The diagnosis Arrival rate exceeds service rate. A queue that grows monotonically is not a queueing problem, it is a capacity problem, and no amount of bufferin
Data-Flow Diagrams
Following the data across trust boundaries rather than the calls.
Architecture Diagrams
Choosing an audience and refusing to mix levels of abstraction.
Backpressure & Flow Control
Telling callers to slow down instead of buffering into congestion collapse.
Change Data Capture
Turning a database's replication log into a stream, and its coupling risk.
Context Diagrams
The system as one box, with its users and external systems.
Data Architecture
General material on structuring, storing and governing data.
Data Classification
Knowing which fields are regulated, because every control depends on it.
Data Governance
Ownership, lineage, quality, catalogues and who may see what.
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 Migration Strategies
Backfill, dual-write, reconciliation and verification.
Data Warehousing
Dimensional modelling, star schemas and analytical workloads.
Deployment Diagrams
What runs where, in which zone, behind which boundary.
Sequence Diagrams
Ordered message exchange, and walking the failure of each arrow.
Streaming Data
Windowing, watermarks, late arrivals and exactly-once semantics.
Application Decomposition
Finding seams in a monolith, starting from the data.