Practise vocabulary for data quality dimensions (completeness, accuracy, timeliness, consistency, uniqueness), data quality rules, expectations, data SLA, and anomaly detection.
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The data quality dimension "completeness" measures:
Completeness example: "email is null for 12% of user records — expected 0% null rate." Or: "pipeline delivered 8,200 rows today; expected 10,000 ± 5%. Missing 18% of expected volume." Completeness checks are the most common first line of data quality monitoring.
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The data quality dimension "timeliness" refers to:
Timeliness is distinct from freshness: fresh data arrived recently; timely data arrived before the deadline. Example: "sales reports must reflect all transactions by 08:00 UTC for the morning leadership review." A pipeline completing at 09:00 with accurate data still fails its timeliness SLA.
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In Great Expectations terminology, an "expectation" is:
Great Expectations (GX) provides a Python framework for writing data expectations as code. Expectations are run as part of pipelines and produce Validation Results: pass/fail per expectation. A Data Doc summarises results for stakeholders. This approach makes data quality testable and version-controlled.
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A "data SLA" (Service Level Agreement) in a data context defines:
Data SLAs make implicit quality expectations explicit and measurable. They enable SLO tracking (Service Level Objectives) and incident alerting: "orders SLA breached — data is 2 hours late". Without data SLAs, consumers discover failures only when reports are wrong, long after the pipeline missed its window.
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Data anomaly detection in the context of data quality lineage means:
ML-based anomaly detection (Monte Carlo, Acceldata, Anomalo) learns baseline distributions for metrics like row count, null rate, and column value ranges — then alerts when actual values deviate significantly. This catches quality regressions that static threshold rules miss, particularly for metrics with seasonal patterns.
What does the "Data Quality & Lineage 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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