# data-patterns

Published articles for data-patterns.

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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>), [DataOps](<https://devfeed.tech/topics/dataops.md>), [Usability](<https://devfeed.tech/topics/usability.md>)

Tags: [data-engineering](<https://devfeed.tech/tags/data-engineering.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>), [decision-making](<https://devfeed.tech/tags/decision-making.md>), [gable](<https://devfeed.tech/tags/gable.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [shift-left](<https://devfeed.tech/tags/shift-left.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [trust](<https://devfeed.tech/tags/trust.md>), [usability](<https://devfeed.tech/tags/usability.md>)

### AI overview

Glassdoor describes a shift from reactive data engineering to a proactive, trust-centered approach to data quality. The article connects organizational culture with technical checks across the data lifecycle.

### 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