Integrating Blockchain with Big Data for Secure Data Sharing: A Comprehensive Methodology

Abstract In today’s data-driven world, the volume of data managed by organizations is growing rapidly, presenting significant challenges in ensuring data security, integrity, and scalability. While blockchain technology offers robust security features, such as immutability and decentralization, it struggles with scalability issues, particularly in high-throughput environments. Conversely, big data frameworks like Hadoop and Spark excel in handling large datasets efficiently but often lack strong security mechanisms. This research proposes a hybrid architecture that integrates the security of blockchain with the scalability of big data frameworks, creating a system capable of securely managing vast amounts of data in real-time. The architecture includes advanced encryption methods, off-chain data management, and seamless integration with existing big data tools, making it suitable for industries such as healthcare, finance, and IoT. Through a comprehensive methodology involving literature review, requirement analysis, architectural design, and performance evaluation, the study demonstrates that this hybrid approach significantly enhances both security and scalability, offering a future-ready solution for secure data sharing across various sectors.