In these modern days, the exponential rise of data has challenged the capabilities of traditional computing systems, requiring creative methods for managing, processing, and analyzing massive quantities. With its built in parallelism and capacity to take use of quantum mechanical processes, quantum computing presents a paradigm shift for big data analytics. It could undergo a revolution to quantum computing’s innovative algorithmic paradigms and intrinsic parallelism. This chapter examines how big data and quantum computing meet, emphasizing algorithmic advancements that improve data driven systems’ accuracy, scalability, and efficiency. It looks at important quantum algorithms that have the potential to transform data clustering, optimization, and pattern recognition, including Grover’s search, Quantum Fourier Transform (QFT), and Quantum Machine Learning (QML) approaches. It also covers practical applications in fields including cyber security, healthcare, and finance, showing how quantum computing may enhance decision making and provide new insights. This chapter examines the relationship between big data and quantum computing, with a particular emphasis on the creation and use of quantum algorithms intended to tackle challenging data problems. We outline some of the most important quantum algorithms, including Grover’s search, the quantum Fourier transform, and quantum machine learning methods, and talk about how they could transform pattern recognition, data processing, and optimization. We discuss the present constraints and potential futures of quantum enabled data analytics while looking at practical uses of quantum algorithms in big data fields like healthcare, finance, and logistics. This concept motivates researchers and practitioners to use quantum computing to solve large data problems in the post Moore’s Law future by bridging the gap between theoretical developments and real world applications. Future research options for combining quantum computing with traditional big data frameworks are also discussed, along with issues with noise, error correction, and hardware constraints.