Real-time Data Mesh Implementation for Optimized Electronics Manufacturing Processes

As a leading enterprise in the electronics manufacturing industry, we face the challenge of effectively managing and analyzing vast amounts of data generated across multiple production lines and facilities. The current centralized data architecture struggles to provide the real-time analytics capabilities necessary for timely decision-making. We propose the development of a data mesh infrastructure to address these limitations and enhance our data strategy. This project involves designing a decentralized data architecture that allows domain-oriented teams to manage their data as a product. By implementing event streaming with Apache Kafka and real-time analytics using Spark, we aim to achieve rapid data processing and improved data flow. Additionally, integrating Snowflake and BigQuery will facilitate scalable data storage and querying capabilities. To ensure smooth workflow orchestration and data pipeline management, tools such as Airflow and dbt will be employed. The successful execution of this project will lead to improved data observability, enabling better monitoring and analysis of production metrics in real-time. This capability is critical for promptly addressing bottlenecks, reducing downtime, and optimizing overall manufacturing efficiency.