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International Journal of Computer Applications (0975 8887) Volume 177 No. 15, November 2019Data Duplication Tactics with Hadoop Was AhmadHongwei Muhammad KhanMubashir TariqTaiyuan University of Technology,
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01
Identify the key fields that should be used to identify duplicates.
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Use data cleansing tools to standardize and clean data before identifying duplicates.
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Utilize fuzzy matching algorithms to identify potential duplicates based on similarities in data.
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Review and manually verify potential duplicates before merging or removing them.
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Implement a process for regularly monitoring and managing duplicates to prevent reoccurrence.

Who needs data duplication tactics with?

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Businesses with large databases that have been collecting data over time from various sources.
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Companies that want to maintain data accuracy and consistency for reporting and analysis purposes.
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Organizations that rely on customer or product information for decision-making and strategic planning.
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Data duplication tactics are typically employed with the intention of improving data quality by identifying and eliminating duplicate records within a dataset.
Data analysts, data scientists, or individuals responsible for data management are typically required to file data duplication tactics within an organization.
Data duplication tactics can be filled out by identifying duplicate entries in a dataset and taking the necessary steps to remove or merge them.
The purpose of data duplication tactics is to ensure data accuracy, improve data quality, and streamline data analysis processes.
Information such as the method used to identify duplicates, actions taken to resolve duplicates, and the impact on data quality should be reported on data duplication tactics.
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