Practise vocabulary for presenting data lineage findings: lineage graph interpretation, column-level lineage, trust scores, and communicating lineage insights to stakeholders.
0 / 5 completed
1 / 5
When a data engineer says "the lineage graph shows", they are about to:
"The lineage graph shows..." is a standard opening phrase when presenting lineage findings to colleagues or stakeholders. It signals that what follows is derived from the actual dependency graph — not from memory or assumption. Examples: "The lineage graph shows that this dashboard's revenue metric traces back through three transformation layers to raw.stripe_events", or "The lineage graph shows 12 downstream models will be affected by this schema change". The phrase establishes evidence-based communication.
2 / 5
"Column-level lineage confirms" is used when:
"Column-level lineage confirms that revenue_usd in the executive dashboard is computed from amount_cents in raw.stripe_charges, divided by 100, filtered where status = 'succeeded', and summed by month" — this is the kind of statement column-level lineage enables. It elevates a data review from table-level ("this comes from Stripe") to field-level precision. Tools that provide column-level lineage: dbt column lineage, Atlan, DataHub, OpenLineage with field-level events.
3 / 5
A "trust score" for a dataset in a lineage review context typically measures:
Trust score (used in Atlan, and conceptually in many governance tools) aggregates signals: recent quality checks passed (%), documentation completeness (%), lineage coverage (are sources documented?), certification status, and usage activity. When presenting lineage findings, referencing a trust score gives stakeholders a shorthand: "The source table has a trust score of 87% — quality checks are passing but documentation is incomplete." It contextualises confidence in the lineage path being reviewed.
4 / 5
Which phrase correctly presents a lineage finding to a non-technical stakeholder?
When presenting lineage findings to non-technical stakeholders, translate graph concepts into business language: "originates from the orders system" instead of "upstream node raw.orders", "passes through two cleaning steps" instead of "intermediate transformation layers", "affect the revenue numbers within 24 hours" instead of "propagate downstream within one pipeline execution cycle". The lineage insight is the same — but the framing matches the audience. Technical precision (options A, C, D) is appropriate for engineer-to-engineer communication.
5 / 5
When documenting lineage review findings in a data incident report, which structure is most appropriate?
A lineage-informed data incident report follows a structured pattern: (1) Affected output — what the business saw (wrong revenue, missing rows), (2) Lineage path — the full graph from source to output, (3) Root cause node — where the issue was introduced (upstream schema change, pipeline failure, bad transformation), (4) Affected scope — downstream datasets confirmed impacted via lineage traversal, (5) Column-level evidence — which specific field carried the error, (6) Remediation — fix at the root cause, then verify downstream datasets correct themselves. This structure enables future root cause analysis and builds organisational lineage knowledge.
What does the "Data Lineage Review Vocabulary" exercise practise?How many questions are in this exercise?
This exercise has 5 questions, each multiple-choice with a full explanation shown after you answer.
What English level is this exercise for?
This exercise is tagged Intermediate. If the vocabulary feels difficult, browse the Data Lineage Vocabulary category page for an easier module to start with.
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