--- title: Where Should a Data Quality Check Live? Gates, Controls,... description: Data quality checks are only useful if they can actually stop bad data from moving forward. The post distinguishes between detection (logging problems) and... canonical: https://daily.dev/posts/where-should-a-data-quality-check-live-gates-controls-and-quarantine-wnb7ormzh twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Where Should a Data Quality Check Live? Gates, Controls, and Quarantine | daily.dev og:description: Data quality checks are only useful if they can actually stop bad data from moving forward. The post distinguishes between detection (logging problems) and... og:url: https://daily.dev/posts/where-should-a-data-quality-check-live-gates-controls-and-quarantine-wnb7ormzh og:image: https://api.daily.dev/og/posts/WNB7OrMzH.png og:image:alt: Where Should a Data Quality Check Live? Gates, Controls, and Quarantine og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Where Should a Data Quality Check Live? Gates, Controls, and Quarantine **[SQL Authority](https://daily.dev/sources/sql-authority)** · 5 min read · 2 upvotes · 0 comments ## Summary Data quality checks are only useful if they can actually stop bad data from moving forward. The post distinguishes between detection (logging problems) and control (blocking bad data), and maps three natural checkpoints in a pipeline: ingestion, transformation, and release gates. It also covers the quarantine pattern — routing bad rows to a holding table instead of discarding the whole batch — and emphasizes that quarantine tables must be actively reviewed. The core advice: place each check at the earliest point where the problem can exist and where it has the power to block, not just log. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the difference between a data quality check that detects problems and one that controls them? Detection is a check that logs or flags a problem without stopping it, like a witness recording an event after the fact. A control is a check that can actually block bad data from moving forward, like a locked door. Pipelines need both, but relying only on detection means problems still reach downstream consumers even after being correctly identified. _Teams debating alerting versus blocking in their pipelines can compare approaches through daily.dev._ ### Should a data quality check block the pipeline or just send a warning? It depends on the severity of the failure: checks catching genuinely damaging issues, like a duplicate in a revenue field, a load at half its expected size, or a missing required column, should hard block the pipeline, since publishing that data is worse than a delay. Minor issues, like a slightly elevated rate of missing optional values, should only raise a warning to avoid alert fatigue from constant false alarms. _daily.dev helps engineers weighing block-versus-warn tradeoffs in their own data validation setups._ ### What is data quarantine and why is it better than rejecting an entire data batch? Quarantine means setting aside only the bad rows that fail a check while letting good rows continue through the pipeline, rather than discarding the whole batch. This keeps valid data flowing and avoids silent data loss, but it only works if someone actually reviews the quarantined rows on a schedule; an unreviewed quarantine table just becomes a growing pile of unresolved bad records. _Engineers designing quarantine workflows can find related pipeline design discussions on daily.dev._ ## Similar posts on daily.dev - [Why data quality matters when working with data at scale](https://daily.dev/posts/why-data-quality-matters-when-working-with-data-at-scale-quxnp688w) · The Next Web · 0 upvotes · 0 comments - [4 Best Practices for Quality Control in Data Management](https://daily.dev/posts/4-best-practices-for-quality-control-in-data-management-udcdpopnx) · Decube · 1 upvotes · 0 comments - [Data Quality and Observability: A Field Guide in Six Parts](https://daily.dev/posts/data-quality-and-observability-a-field-guide-in-six-parts-lnhl1rihc) · SQL Authority · 0 upvotes · 0 comments - [The Top 3 Data Quality Practices for Successful AI Application Development](https://daily.dev/posts/the-top-3-data-quality-practices-for-successful-ai-application-development-kpwq2gjby) · SD Times · 0 upvotes · 0 comments --- Tags: [#devops](https://daily.dev/tags/devops), [#sql](https://daily.dev/tags/sql), [#data-engineering](https://daily.dev/tags/data-engineering), [#data-quality](https://daily.dev/tags/data-quality), [#etl](https://daily.dev/tags/etl) [View this post on daily.dev](https://daily.dev/posts/where-should-a-data-quality-check-live-gates-controls-and-quarantine-wnb7ormzh) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Where Should a Data Quality Check Live? 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