Loading...
“Big data” describes demands that exceed a chosen system’s convenient capacity or processing model. Size matters, but structure and the time available to respond can matter just as much.
What you'll be able to do
Volume concerns the amount to store or process. Velocity concerns arrival and response rate, such as a continuous sensor stream. Variety concerns different forms and structures, including tabular records, text and media. A dataset can be challenging through several dimensions rather than a single universal byte threshold.
Cleaning, interpreting and connecting heterogeneous values can be harder than merely retaining them. State the workload: an overnight archive and a real-time alert system have different latency requirements even with similar daily data volume. Data quality and permitted use remain important at large scale.
A fact-based representation records individual assertions, for example a person taking a course. A graph schema describes allowed kinds of nodes, edges and properties. Nodes can represent people and courses; edges represent enrolment, while properties store related identifiers or dates.
A schema defines how information is organised, not proof that every stored assertion is true. Repeated or conflicting facts may need provenance and reconciliation. Graph relationships can support connected queries, but the best representation depends on the required access patterns rather than on a fashionable label.
A fixed relational row/column design can be awkward for highly varied or rapidly changing data, especially when the chosen platform’s capacity is exceeded. Alternative or combined representations can make some workloads easier. However, relational systems can also support large and distributed deployments; “relational can never scale” is too absolute.
The specification’s contrast should motivate analysis of schema, workload and distribution constraints. It does not mean every large dataset needs machine learning or that a graph automatically solves every quality and performance problem. Explain why the chosen design fits the actual question.
| Graph element | Example |
|---|---|
| Node type | Student |
| Node property | StudentID |
| Edge type | ENROLLED_IN |
| Target node type | Course |
Attempt the task before opening its worked response. Record each change of state, explain the rule used, and check the result against the original requirements. Code examples labelled Python 3 use that language’s conventions; pseudocode states its own assumptions.
Common mistakes to avoid
Key takeaways
Test yourself
Answer a few questions, then review anything you missed.
Start with the quiz above to check the key ideas. You can also choose another topic from the practice library.