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The Race For Data Quality in a Medallion Architecture

DataKitchen

The Race For Data Quality In A Medallion Architecture The Medallion architecture pattern is gaining traction among data teams. It is a layered approach to managing and transforming data. It sounds great, but how do you prove the data is correct at each layer? Bronze layers should be immutable.

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Data architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Data Architecture and Strategy in the AI Era

Cloudera

At a time when AI is exploding in popularity and finding its way into nearly every facet of business operations, data has arguably never been more valuable. As organizations continue to navigate this AI-driven world, we set out to understand the strategies and emerging data architectures that are defining the future.

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Perform data parity at scale for data modernization programs using AWS Glue Data Quality

AWS Big Data

Today, customers are embarking on data modernization programs by migrating on-premises data warehouses and data lakes to the AWS Cloud to take advantage of the scale and advanced analytical capabilities of the cloud. Data parity can help build confidence and trust with business users on the quality of migrated data.

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Data integrity vs. data quality: Is there a difference?

IBM Big Data Hub

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. In short, yes.

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Breaking State and Local Data Silos with Modern Data Architectures

Cloudera

Data is the fuel that drives government, enables transparency, and powers citizen services. That should be easy, but when agencies don’t share data or applications, they don’t have a unified view of people. Legacy data sharing involves proliferating copies of data, creating data management, and security challenges.

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What is a Data Mesh?

DataKitchen

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. DataOps helps the data mesh deliver greater business agility by enabling decentralized domains to work in concert. . But first, let’s define the data mesh design pattern.