Real-Time Data Pipeline Implementation for Enhanced Client Insights

As a small-to-medium enterprise in the wealth management sector, we face the challenge of staying competitive while providing personalized investment strategies to our clients. This project aims to build a real-time data pipeline leveraging technologies such as Apache Kafka for event streaming, Spark for distributed data processing, and Airflow for orchestrating data workflows. By implementing a data mesh architecture, we aim to decentralize our data ownership and operations, allowing different teams to benefit from isolated yet interconnected data units. Furthermore, integrating MLOps practices ensures our machine learning models remain reliable and effective. We will also employ data observability tools to monitor and manage the data pipeline's performance and ensure data quality. The ultimate goal is to enrich our client insights, providing them with tailored financial advice that aligns with their individual goals. Successful implementation of this project will enhance decision-making processes, improve client satisfaction, and drive business growth.