Implementing a Real-time Data Engineering Platform for Enhanced Operational Efficiency in Electronics Manufacturing

As an enterprise leader in electronics manufacturing, we face challenges in efficiently handling the vast amounts of data generated across our production lines. Our goal is to implement a state-of-the-art data engineering platform that leverages real-time analytics to streamline operations and enhance productivity. The project will involve setting up an event-streaming infrastructure using Apache Kafka to capture data in real-time from various sources. Spark will be employed for processing and analyzing the data, while Airflow will orchestrate the data pipelines. By adopting a data mesh architecture, we aim to decentralize data ownership and enhance cross-domain collaboration. This approach will ensure that each domain within our operations can manage its own data, fostering innovation and agility. Additionally, data observability tools will be integrated to provide comprehensive insights into data quality and pipeline performance. The platform will ultimately reside on a cloud-based environment like Snowflake or BigQuery, ensuring scalability and accessibility. This strategic move is intended to give us a competitive edge by enabling faster, data-driven decision-making and improving our manufacturing throughput.