Real-time Data Pipeline for Predictive Maintenance in Electronics Manufacturing

As an industry leader in electronics manufacturing, we are facing challenges with unplanned machine downtimes that disrupt production schedules and increase operational costs. The solution is to implement a real-time data pipeline that collates data from multiple production lines and equipment sensors into a unified analytics platform. The project involves designing and deploying a data mesh architecture using state-of-the-art technologies like Apache Kafka for event streaming and Apache Spark for processing large-scale data. Our aim is to leverage tools such as Airflow for workflow automation, dbt for data transformations, and Snowflake or BigQuery for scalable data warehousing. By incorporating MLOps practices, we will enable continuous integration and deployment of machine learning models that predict maintenance needs, thereby reducing downtime and improving efficiency. This project is critical not only for operational improvements but also to maintain our competitive edge through innovative data use.