<p>The transition to Public Procurement 4.0 demands advanced data-driven solutions to understand difficulties, including inefficiencies, the absence of transparency, and fragmented data management across the procurement lifecycle. Existing Big Data frameworks and analytics technologies offer promising capabilities but are often siloed and lack integration customized to the particular requirements of public procurement. This paper explores the state-of-the-art Big Data frameworks and analytics technologies, critically analyzing their potential applications in public procurement. Building on this exploration, the study proposes a comprehensive Big Data pipeline designed to meet the requirements of Public Procurement 4.0. The proposed pipeline integrates batch and stream processing, historical data management, and advanced analytics to provide end-to-end data flow capabilities. Technologies involving Apache Kafka, Spark, Flink, Elastic Search, and Azure Blob Storage are provided to swallow, operate, keep, and envision structured, semi-structured, and unstructured data from multiple sources, such as portals of tendering, IoT devices, and platforms of social media. Through arranging with the particular requests of public procurement, including the assessment of supplier effectiveness, analysis of spend, approval observation, and green procurement, the pipeline secures actionable perception for stakeholders over all phases of the procurement lifecycle. This research offers to the cognitive development body by contributing to a unified framework that combines present technologies, connecting study spaces, and authorizing the identification of smart, adequate, and transparent systems of public procurement.</p>