Real-Time Data Infrastructure Enhancement for Food Processing Efficiency

In the competitive landscape of food processing, timely decision-making is critical to optimizing efficiency and minimizing waste. Our company, a rapidly growing player in the industry, is looking to revolutionize our data capabilities by leveraging the latest advancements in data engineering. We aim to build a robust real-time data infrastructure that will seamlessly integrate with our existing systems and provide granular insights into our processing operations. The project will involve setting up a data mesh architecture to decentralize data ownership and enhance scalability across departments. We seek to implement event streaming using Apache Kafka to capture real-time data from various sensors and systems across our production lines. By utilizing Apache Spark and Airflow, we will process and orchestrate these data streams efficiently. Furthermore, the curated data will be stored and queried using modern cloud data warehouse solutions like Snowflake and BigQuery, ensuring that our analytics and reporting are both reliable and scalable. To maintain high-quality insights, dbt will be employed to manage data transformations and ensure data observability. The ultimate aim is to facilitate real-time analytics that empower decision-makers, reduce downtime, and enhance product quality.