Eliminating Data Redundancy and Inconsistencies in Scalable Applications

Eliminating Data Redundancy and Inconsistencies in Scalable Applications

Mahesh Mokale

Independent Researcher

Email: maheshmokale.mm[at]gmail.com

Abstract: Scalable applications face unique challenges as they grow in terms of both data volume and user concurrency. Among the most critical issues are data redundancy—where the same data is stored in multiple places unnecessarily—and data inconsistencies, which occur when duplicated data becomes unsynchronized or contradictory. These issues not only lead to increased storage costs and decreased performance but also compromise the integrity and trustworthiness of the application. With the rise of distributed architectures, microservices, and polyglot persistence, ensuring consistent and non-redundant data management has become more difficult yet more important than ever. Organizations often struggle with maintaining a single source of truth across multiple services, data pipelines, and storage systems, which leads to brittle systems, complicated debugging, and user-facing errors. In multi-tenant or high-concurrency systems, even minor inconsistencies or redundancies can quickly propagate and amplify, making recovery expensive and time-consuming. This paper provides an in-depth analysis of the root causes of data redundancy and inconsistencies within scalable systems and presents a comprehensive set of solutions for addressing them. It explores database normalization techniques, distributed consistency models, and service-oriented architectures to reduce redundancy. Furthermore, it discusses how to mitigate inconsistencies through strong consistency mechanisms, transactional safeguards, and schema versioning. The role of data governance, metadata management, and domain ownership is also emphasized to ensure long-term maintainability. Drawing from widely accepted architectural patterns and real-world case studies from companies like Uber and Netflix, this paper offers actionable insights and best practices that can be applied by developers, architects, and engineering leaders to design robust and maintainable scalable applications. The research and recommendations presented are based on developments and industry practices up to the year 2023, providing a current and practical guide to one of the most pressing challenges in modern software engineering.

Keywords: data redundancy, data inconsistency, scalable applications, microservices, distributed systems, normalization, domain-driven design, metadata management, eventual consistency, schema versioning, master data management, strong consistency, event sourcing, CQRS, transactional outbox, Apache Kafka, Apache Atlas, Uber, Netflix, service-oriented architecture, data governance, data synchronization, polyglot persistence, consistency models, transactional integrity, data validation, data lineage, metadata catalog, source of truth, fault tolerance, consensus algorithms, Apache ZooKeeper, Avro, Protobuf, JSON Schema, Amundsen, Apache Hadoop, change data capture, observability, chaos engineering, idempotency, retry mechanisms, API integration, asynchronous communication