Apply controls, rules, and remediation practices to keep data reliable throughout data pipelines.
This deck is for anyone working on best practices related to Data Engineering, from beginner to intermediate.
You will work on points such as: In data quality, what does consistency primarily assess across datase… · What does the uniqueness data-quality dimension focus on for a key co… · In data quality, what does the completeness dimension specifically me….
1In data quality, what does consistency primarily assess across datasets?
Answer: Whether logically related values agree across sources and tables.
2What does the uniqueness data-quality dimension focus on for a key column?
Answer: Whether key values are free of duplicates across records.
3In data quality, what does the completeness dimension specifically measure?
Answer: The proportion of required fields that are actually populated.
4What does the validity data-quality dimension evaluate for a single field?
Answer: Whether a value conforms to defined rules or allowed domains.
5What does the freshness dimension of data quality measure in a pipeline?
Answer: How recently data was produced compared with an expected time window.
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