Real-Time Data Pipeline Implementation for Enhanced Manufacturing Efficiency

As a burgeoning pharmaceutical manufacturing startup, we face the challenge of ensuring optimal efficiency and compliance with stringent industry regulations. Our goal is to develop a real-time data pipeline that captures and processes data from various manufacturing stages, enabling instantaneous analytics and decision-making. We aim to integrate technologies such as Apache Kafka for event streaming, Spark for processing large datasets in real-time, and Snowflake for scalable data storage. The use of Airflow will automate our data workflows, while dbt will facilitate data transformation. This setup will allow us to achieve a data mesh architecture, promoting decentralized data management and enhancing data observability. By leveraging these technologies, we anticipate improvements in operational efficiency, reduced downtime, and enhanced regulatory compliance. The project is budgeted between $5,000 and $25,000 and is expected to be completed within 4-6 weeks, with a high level of urgency due to immediate operational needs.