Remove Data Profiling Remove Data Quality Remove Hadoop
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Data Integrity for AI: What’s Old is New Again

Precisely

Each source system had their own proprietary rules and standards around data capture and maintenance, so when trying to bring different versions of similar data together such as customer, address, product, or financial data, for example there was no clear way to reconcile these discrepancies. Then came Big Data and Hadoop!

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Data Quality Framework: What It Is, Components, and Implementation

DagsHub

As such, the quality of their data can make or break the success of the company. This article will guide you through the concept of a data quality framework, its essential components, and how to implement it effectively within your organization. What is a data quality framework?

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How data engineers tame Big Data?

Dataconomy

Data engineers play a crucial role in managing and processing big data Ensuring data quality and integrity Data quality and integrity are essential for accurate data analysis. Data engineers are responsible for ensuring that the data collected is accurate, consistent, and reliable.