A Comparative Review Of Data Representation And Data Types In Modern Programming Languages

The representation and classification of data remain central to the design, implementation, and evolution of modern programming languages. This study presents a comprehensive comparative review of data types and data representation across contemporary programming paradigms, situating current practices within their historical and theoretical foundations. Drawing from systematic literature spanning 2015-2025 and guided by PRISMA-based evidence synthesis, the review examines primitive, composite, and advanced data types, alongside the underlying type systems that govern their behavior. The analysis highlights the dual role of data types as both restrictive and enabling constructs, tracing their evolution from early languages such as FORTRAN and ALGOL to modern systems such as Python, Java, and Rust. Key distinctions between static and dynamic typing, as well as strong versus weak type enforcement, are critically evaluated, revealing inherent trade-offs between flexibility, safety, and performance. Furthermore, the review synthesizes recent advances in type system design, including Generalized Algebraic Data Types (GADTs), ownership-based memory models, and declarative collection abstractions, demonstrating their impact on program correctness and efficiency. The study also extends the discussion to modern data-intensive domains, where data representation frameworks underpin developments in array programming, multimodal data integration, and machine learning architectures. Despite noteworthy progress, the findings reveal persistent gaps between theoretical advances in type systems and their practical application in data science workflows. The divergence between safety-oriented systems programming and flexibility-driven data analytics environments shows the need for integrative approaches such as gradual typing, schema-aware type systems, and shape-polymorphic abstractions. This review contributes to the discourse by providing a unified perspective on data types and representation, offering insights that are critical for advancing programming language design and data-centric computing.