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Este documento explora el problema de limpiar y fusionar bases de datos con información sobre entidades comunes, abordando el desafío de la duplicación de datos y presentando un sistema de limpieza

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What is real-world data is dirty?

Real-world data that is dirty refers to data that is incomplete, inaccurate, or inconsistent. It may contain errors, missing values, or duplicated entries.

Who is required to file real-world data is dirty?

There is no specific requirement to file real-world data that is dirty as it is not a formal term or process. However, data cleaning and validation are essential tasks for anyone working with real-world data.

How to fill out real-world data is dirty?

Real-world data is not filled out as it is collected from various sources. However, to clean and validate real-world data, techniques such as data cleaning, data normalization, and data deduplication can be used.

What is the purpose of real-world data is dirty?

The purpose of cleaning and validating real-world data is to ensure its accuracy, completeness, and consistency. This helps in making informed decisions, conducting accurate analysis, and improving the overall quality of data-driven applications.

What information must be reported on real-world data is dirty?

There is no specific information to be reported on real-world data that is dirty. However, it is important to identify and address any data quality issues to ensure the reliability and integrity of the data.

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