Real-Time Data Pipeline Optimization for Enhanced Food Processing Efficiency

With an increase in production volumes and a push towards operational efficiency, our enterprise in the food processing industry faces the challenge of managing large-scale data effectively. We are looking to implement a real-time data pipeline that can process and analyze data streams from various production lines across our facilities. The goal is to enhance our ability to make data-driven decisions rapidly, thereby optimizing resource allocation, reducing waste, and improving overall production quality. The project will involve integrating Apache Kafka for event streaming, Spark for real-time data processing, and Airflow for workflow management, alongside BigQuery for data warehousing. Additionally, leveraging technologies such as dbt for data transformation and Databricks for advanced analytics will be key components. The successful candidate will provide a comprehensive strategy that ensures data observability and quality, aligning with our long-term data mesh strategy and MLOps frameworks. This will empower teams to access and utilize data efficiently, promoting an innovative culture of data-driven decision-making across the organization.