# gable

Published articles for gable.

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## Data Quality at Petabyte Scale: Building Trust in the Data Lifecycle

DevFeed: [Data Quality at Petabyte Scale: Building Trust in the Data Lifecycle](<https://devfeed.tech/articles/data-quality-at-petabyte-scale-building-trust-in-the-data-lifecycle-22608.md>)

Original publisher: [Read original article](<https://medium.com/glassdoor-engineering/data-quality-at-petabyte-scale-building-trust-in-the-data-lifecycle-7052361307a4?source=rss----288d984af747---4>)

Author: Zakariah Siyaji

Published: 2025-02-14T15:52:43Z

Content type: article

Language: en

Sources: [Glassdoor Engineering](<https://devfeed.tech/sources/glassdoor-engineering.md>)

Topics: [Data Quality](<https://devfeed.tech/topics/data-quality.md>), [data-engineering](<https://devfeed.tech/topics/data-engineering.md>), [data-processing](<https://devfeed.tech/topics/data-processing.md>), [CI/CD](<https://devfeed.tech/topics/cicd.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>), [schema-evolution](<https://devfeed.tech/topics/schema-evolution.md>), [AI Integration Strategies](<https://devfeed.tech/topics/ai-integration-strategies.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>)

Tags: [2](<https://devfeed.tech/tags/2.md>), [3](<https://devfeed.tech/tags/3.md>), [about](<https://devfeed.tech/tags/about.md>), [adopting](<https://devfeed.tech/tags/adopting.md>), [airbnb](<https://devfeed.tech/tags/airbnb.md>), [approach](<https://devfeed.tech/tags/approach.md>), [area](<https://devfeed.tech/tags/area.md>), [article](<https://devfeed.tech/tags/article.md>), [building](<https://devfeed.tech/tags/building.md>), [building-trust](<https://devfeed.tech/tags/building-trust.md>), [categories](<https://devfeed.tech/tags/categories.md>), [challenges](<https://devfeed.tech/tags/challenges.md>), [code](<https://devfeed.tech/tags/code.md>), [data](<https://devfeed.tech/tags/data.md>), [data-patterns](<https://devfeed.tech/tags/data-patterns.md>), [data-platform-engineering](<https://devfeed.tech/tags/data-platform-engineering.md>), [data-quality](<https://devfeed.tech/tags/data-quality.md>), [gable](<https://devfeed.tech/tags/gable.md>), [lifecycle](<https://devfeed.tech/tags/lifecycle.md>), [petabyte](<https://devfeed.tech/tags/petabyte.md>), [quality](<https://devfeed.tech/tags/quality.md>), [scale](<https://devfeed.tech/tags/scale.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [trust](<https://devfeed.tech/tags/trust.md>)

### AI overview

Glassdoor describes shifting data quality work upstream so product engineering teams share responsibility with data consumers. Data contracts specify schemas, constraints, and compatibility expectations; Gable.ai static analysis in CI/CD can warn on or block contract-breaking changes, while its fine-tuned LLM is intended to flag subtler business-logic errors that contracts miss. At Kafka ingestion, Confluent Schema Registry validates events and sends violations to dead-letter queues; DataDog provides topic-level monitoring for anomalies and volume shifts. Glassdoor also distinguishes hard checks that halt pipelines from soft checks that surface possible drift for context, and uses a write-audit-publish flow: stage data, audit it, then promote validated data to production. The article presents these measures as complementary ways to catch problems earlier and build shared trust. Its account includes planned Gable.ai lineage and privacy-compliance capabilities, rather than reporting them as already fully operational.

### Source excerpt

The data Lifecycle with Data Quality Checks at GlassdoorMotivation Glassdoor has transformed from an employee review site to a community for workplace conversations [1]. As our platform evolves to support content creators, facilitate discussions, and offer rich content, it has become more apparent than ever that adopting a data-driven culture is essential. Businesses rely on accurate, high-quality data to understand their operations and assess strategic outcomes. Flawed or incomplete data results in misguided decisions and undermines trust. Recognizing this risk, we made data quality a foundational principle of our data-driven transformation. Although every company defines data quality differently, there is a universal expectation that data used for decision-making must be trustworthy. Additionally, data quality challenges are not solely technical; a psychological component is closely linked to trust in data. Airbnb recognized this and sought to develop a scoring system that acknowledges the belief that data quality is a multivariate issue, encompassing accuracy, reliability, stewardship, and usability, along with more detailed dimensions within each of these categories [2]. On the other hand, Netflix employs a more technically centered approach to quality: data is initially written to a temporary staging area, audited, and then published to the production location upon passing quality checks [3]. Ultimately, Glassdoor drew inspiration from these lessons and aimed to reinforce trust through a cultural shift and a series of technical solutions. This article demonstrates how a proactive, trust-centered approach that connects data producers and consumers establishes a foundation for more rigorous data quality methods, ultimately bolstering a strong company-wide strategy. Figure 1. Enhancing quality guards at the application code layer.Culture Shift: Reactive to Proactive Historically, Glassdoor's data engineering teams have been reactive, learning about issues only aft