Real-Time Data Pipeline Implementation for Enhanced Production Insights

Our SME food processing company is experiencing challenges in efficiently managing and analyzing production data in real-time. We currently rely on batch processing systems that delay decision-making and hinder our ability to respond quickly to production anomalies. To address this, we are looking to implement a real-time data pipeline that integrates various data sources across the production floor. The solution will utilize Apache Kafka for event streaming, Databricks for scalable processing, and Snowflake for data storage and analytics. This project aims to provide instant insights into production efficiency, detect waste trends, and enhance quality control measures. Additionally, the integration of MLOps practices will ensure continuous improvement and adaptability of machine learning models used in analytics. The successful completion of this project will empower our operations team with actionable insights, leading to reduced waste, improved product quality, and increased production efficiency.