Enhancing Data Management Strategies with a Hybrid Layering Framework in Assessing Data Validation and High Availability Sustainability

Abstract

Individuals and digital organizations deal with a substantial amount of collected data required for performing various data management strategies, such as replacing, upgrading, and migrating existing data from one system to another, while supporting the data’s complexity, authenticity, quality, and precision. Failures in data migration can result in data and service interruptions, financial losses, and reputational harm. This research aims to identify the specific challenges of a data management strategy, develop a comprehensive framework of data migration practices, and assess the efficacy of data validation and high availability for optimizing complex data and reducing the need to minimize errors during data migration. Combining trickle and zero-downtime migration techniques with a layering approach, a hybrid-layering framework was designed to encompass the entire spectrum of data migration techniques, beginning with system requirements and data transformation, rigorous functions, and evaluation metrics for sustainable data validation. The evaluation metric criteria are defined to evaluate data migration based on data consistency, integrity, quality, accuracy, and recall. The experiment demonstrated a real-world scenario involving a logistics company with 222 tables and 4.65 GB of data. The research compared various data migration strategies. The outcomes of the hybrid-layering framework’s examination of the final system’s functionality are satisfactory, emphasizing the critical importance of data migration sustainability to ensure data validity and high availability. This study is useful for individuals and organizations seeking to sustainably improve their data management strategies to minimize disruptions while preserving data integrity.