Real-Time Data Pipeline for Quality Control Optimization in Pharmaceutical Manufacturing

In the pharmaceutical manufacturing industry, maintaining quality control is not only critical for compliance with regulations but also essential for ensuring product efficacy and safety. Our startup is confronted with the challenge of managing large volumes of data generated from various stages of the manufacturing process. We are looking to develop a robust, real-time data pipeline that can integrate multiple data sources, provide real-time analytics, and enable predictive insights to improve our quality control measures. The project will leverage Apache Kafka for event streaming, Spark for distributed data processing, and Airflow for orchestrating data workflows. Additionally, technologies such as dbt for data transformation, and Snowflake or BigQuery for data warehousing will be integrated to ensure efficient data storage and accessibility. By implementing a data mesh architecture, we aim to decentralize data ownership and foster collaboration across departments, ultimately enhancing our decision-making capabilities and ensuring compliance with industry standards.