Big Data – A-level Computer Science exam questions with mark scheme

1Core3 marks

Big data is often described using three characteristics. Name these three characteristics and describe each one. [3]

Volume: there is too much data to fit on (or be processed by) a single server/computer (allow: too much data to store or process with conventional/traditional database tools). Do not accept just ‘a lot of data’, which only restates the name. Name and description both needed for each mark(1)
Velocity: data is created/arrives or changes at high speed (e.g. streamed), so it must be processed/responded to quickly, within milliseconds to seconds (either the high rate of arrival/change or the need for a very fast response is enough with the name)(1)
Variety: data comes in many different forms, structured and unstructured (e.g. text, images, video, sensor readings). Ignore other characteristics such as veracity or value(1)
Answer: Volume (too large for a single server), velocity (arrives and must be processed very quickly), variety (many forms, structured and unstructured)
Examiner tip: Name each V and describe it without just restating the word: for volume say the data is too large to fit on a single server, not just ‘a lot of data’.

2Standard4 marks

Big data is often stored using the fact-based model rather than a relational database.

(a) Describe the fact-based model. [3]

(b) Give one reason why it is suited to big data. [1]

(a) Each fact records a single, atomic piece of information (it cannot be broken down further). For any one of the three (a) marks, allow: each fact is uniquely identifiable (so two otherwise identical facts can be told apart)(1)
(a) Each fact is timestamped with when it was recorded / became true (allow: a fact is always true, as it is true at its timestamp)(1)
(a) Facts are immutable: they are never changed or deleted; when something changes a new fact is added (allow: data is only ever appended)(1)
(b) Any one: facts are only ever added, never updated, so the data can be spread across many servers and processed in parallel without update conflicts; the full history is kept so data lost to errors can be recovered or queried at any past time; new kinds of fact can be added without changing a fixed schema(1)
Answer: (a) Data is stored as atomic facts, each timestamped, which are immutable (never updated or deleted, only added) (b) e.g. append-only facts are easy to distribute across many servers without update conflicts / full history kept
Examiner tip: Immutable means no update or delete; a changed address is a new fact.

3Standard4 marks

Explain why functional programming is well suited to processing big data. [4]

Functions are stateless / have no side effects: the same input always gives the same output (allow: pure functions / referential transparency)(1)
Data structures are immutable, so no process can change data another process is using (no shared state, no race conditions or locking)(1)
So the processing can be split across many processors/servers and run in parallel (distributed), which big data needs because it will not fit on one server(1)
Higher-order functions such as map, filter and fold/reduce can be applied to each part of the data independently and the results combined (allow: makes it easier to write correct, efficient distributed code)(1)
Answer: Functions have no side effects and data is immutable, so there is no shared state; the data can therefore be split across many servers and processed in parallel safely, using higher-order functions such as map and reduce on each part.
Examiner tip: Explain the link: no shared state is what makes parallel execution safe.

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