Real-Time Data Mesh Infrastructure for Enhanced Customer Insights

As a growing player in the luxury goods market, our company is keen to leverage cutting-edge data technologies to enhance our operational capabilities and customer understanding. The project involves designing and implementing a robust data mesh infrastructure that enables seamless, real-time data streaming across various business units. By utilizing Apache Kafka for event streaming and Spark for real-time data processing, we aim to integrate these capabilities with our existing platforms such as Snowflake and BigQuery for effective data storage and retrieval. A vital component of this initiative is implementing MLOps to streamline our machine learning models and ensure they are consistently optimizing customer insights and sales strategies. Additionally, using tools like dbt and Airflow will automate data workflows, enhancing our ability to make data-driven decisions quickly. By achieving data observability, we can monitor and ensure the accuracy and reliability of our data across various pipelines. This project is essential as it directly feeds into our strategic goal of tailoring luxury experiences to customer preferences, increasing our market share, and maintaining competitive advantage.