Addressing Data Quality and Consistency Issues in Cloud-Based Big Data Environments

This research discusses the solutions to data quality and consistency problems encountered in the cloud environments. It first describes how automated data validation and cleansing were captured using machine learning algorithms that can detect and correct anomalies in real-time. Rich data lineage, traceability and enhanced data profiling and metadata management. Additionally, distributed consensus protocols, such as Paxos and Raft, were proposed to provide consistency to data stored in geographically distributed cloud storage infrastructure, potentially reducing latency and synchronization issues. The inherently heterogeneous nature of big data, alongside the need for fast, reliable access to it instead, supports approaches incorporating structured and unstructured data methods in hybrid storage architectures. In addition, measures can be taken to enforce standardization of data quality metrics and govern compliance with the regulatory requirements using frameworks for cloud-native data governance. Moreover, this work advocates for the implementation of scalable monitoring tools in order to perform regular consistency checks on data, as well as the deployment of self-repair mechanisms to recover from inconsistencies. Such a commitment will guarantee improved data quality and, hence, more excellent reliability of the data-driven insights that streamline their operations and can lead to informed business decisions that drive improved performance and profitability.