Enhancing Scalability in DNA Data Storage: Computational Methods for Efficient Encoding, Retrieval, and Petabyte-Scale Storage

As technology advances, the global datasphere is experiencing exponential growth, with projections exceeding 200 zettabytes by 2026. This data surge stems from platforms like social media, IoT, AI/ML processing, video streaming, cloud computing, and e-commerce. Data storage methods have evolved significantly, transitioning from magnetic storage (hard disks, tapes) and optical storage (CDs, DVDs) to solid-state drives, cloud storage, and databases. However, these traditional methods have limitations, including e-waste generation, data loss, high costs, and privacy concerns. Enter DNA storage, a biologically inspired paradigm offering unparalleled storage density, durability, longevity, and sustainability. DNA encodes, synthesizes, stores, and retrieves data through sequencing as a natural information carrier. This innovative approach addresses current challenges while providing energy efficiency and eliminating e-waste. Enhanced modifications in the scalability of DNA storage highlight its potential as a transformative solution for the data-driven future. Additionally, the paper explores computational modules, such as optimized binary-to-nucleotide encoding schemes and error-resilient algorithms, alongside cognitive intelligence strategies to enhance retrieval accuracy and scalability.