The increasing necessity of efficient and effective agriculture has pushed towards the development of computer aided techniques, where on-field measurements are used to take objective decisions to optimize the production, giving birth to data-driven agronomy. With the diffusion of 5G-based IoT devices it becomes possible to deploy a variety of sensors in large amounts, enabling continuous collection of monitoring data. Agronomists necessitate the adoption of Big Data techniques and technologies to handle such large amount of data. These solutions provide powerful tools to analyze and model the complexity of the field, i.e. applying statistics and Machine Learning based methods to the product processes. In this paper, we propose a Big Data infrastructure that integrates with 5G-enabled sensors, providing scalable data ingestion, pipelining and information querying capabilities. We also show a practical scenario where our infrastructure has been implemented and report preliminary results on its performance.