Real-time Data Mesh Implementation for Enhanced Pharmaceutical Production Insights

In an era where data-driven insights drive competitive advantage, our pharmaceutical manufacturing division recognizes the imperative need to transform its data infrastructure. The project focuses on establishing a real-time data mesh architecture to amalgamate and analyze data from multiple production lines, R&D departments, and supply chain points. This system will enable the seamless exchange of data across various siloed departments, facilitating improved decision-making and operational efficiency. Leveraging technologies such as Apache Kafka and Spark for event streaming and real-time processing, alongside advanced data management tools like Snowflake and BigQuery, we aim to develop a robust, scalable platform. The project will also incorporate MLOps frameworks to deploy machine learning models that predict batch failures, optimize resource allocation, and ensure compliance with industry regulations. By transitioning to this advanced data mesh, we anticipate a decrease in production errors, faster time-to-market for new products, and enhanced compliance with regulatory standards.