ellibsЭлектронные библиотекиRussian Digital Libraries Journal1562-5419Казанский (Приволжский) федеральный университет10.26907/1562-5419-2022-25-2-159-178ellibs-326Research ArticleСтатьиРазработка модуля проверки данных для удовлетворения метрики устареванияDevelopment a Data Validation Module to Satisfy the Retention Policy MetricСибгатуллинаА. И.SibgatullinaA. I.aigul.sibgatulli@gmail.comЯкуповА. Ш.YakupovA. S.azat.yakupov@it.kfu.ruКазанский (Приволжский) Федеральный университетKazan (Volga region) Federal University202228042022252159178Copyright © Сибгатуллина А.И., Якупов А.Ш., 20222022Сибгатуллина А.И., Якупов А.Ш.Sibgatullina A.I., Yakupov A.S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ellibs.elpub.ru/jour/article/view/326

Из года в год возрастает объем мирового рынка больших данных. Их анализ является неотъемлемой частью для принятия немедленных и надежных решений. Технологии больших данных ведут к значительному снижению стоимости за счет использования облачных сервисов, распределенных файловых систем, когда возникает потребность в хранении больших объемов информации. Их аналитика неразрывно связана с понятием качества данных, что особенно важно, если они имеют определенный срок хранения – метрику устаревания – и мигрируют из одного источника в другой, увеличивая риск потери данных. Предупреждение негативных последствий достигается за счет процесса сверки данных – комплексной проверки больших объемов информации с целью подтверждения их согласованности. В статье рассмотрены вероятностные структуры данных, которые могут быть использованы для решения задачи, а также предложена реализация – модуль проверки целостности данных с использованием фильтра Блума с подсчетом. Данный модуль интегрирован в Apache Airflow для автоматизации процесса.

Every year the size of the global big data market is growing. Analysing these data is essential for good decision-making. Big data technologies lead to a significant cost reduction with use of cloud services, distributed file systems, when there is a need to store large amounts of information. The quality of data analytics is dependent on the quality of the data themselves. This is especially important if the data has a retention policy and migrates from one source to another, increasing the risk of a data loss. Prevention of negative consequences from data migration is achieved through the process of data reconciliation – a comprehensive verification of large amounts of information in order to confirm their consistency. This article discusses probabilistic data structures that can be used to solve the problem, and suggests an implementation – data integrity verification module using a Counting Bloom filter. This module is integrated into Apache Airflow to automate its invocation.

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The authors declare that there are no conflicts of interest present.