Real-Time Security & Analytics: Optimizing Cloud Data Mesh Architecture for Low-Latency Reporting and Immutable Data Security.

Abstract

Cloud computing platforms have been very useful in Swift run time processing of bulk amounts of distributed data in modern enterprises. Meanwhile, they should provide good data security, integrity and auditability to address needs in operational level and regulatory level. The conventional centralized multi-cloud information designs tend not to satisfy these two conditions, as it adds points of bottlenecks within the latency and causes a single security failure point. The paper explores the quantitative way that an optimized Cloud Data Mesh architecture can be used to support both low-latency and immutable data security.

The suggested solution takes data as a domain product and integrates both performance and security controls into pipes of decentralized data. The reduction of the reporting latency is done using real-time streaming ingestion, domain-specific data contracts, and optimized analytical access patterns. There is no possibility of modifying data, since cryptographic hashing and ledger-style audit systems are applied on a domain level. The experimental design is a controlled experiment that likens a bursting Data Mesh architecture setup to a centralized baseline data lake based on a cloud.

There is a statistically significant improvement in ingestion latency, query response time, throughput and tamper detection speed. The results indicate that secure-by-design and decentralized data architecture can provide real operational intelligence without any data integrity and governance losses. This paper is the empirical evidence of Cloud Data Mesh that could effectively be discussed as a scalable and secure platform of real-time cloud analytics.