Real-Time Data Pipeline Transformation for Commercial Real Estate Insights

As a commercial real estate SME, we manage a diverse portfolio that requires efficient data handling to optimize asset performance and improve decision-making. Currently, our data collection methods are outdated and lack real-time processing capabilities, leading to delayed insights that hinder strategic planning. The goal of this project is to build a scalable, real-time data pipeline using technologies like Apache Kafka for event streaming, Spark for data processing, and Airflow for workflow management. By deploying a data mesh architecture, we aim to decentralize data ownership, allowing various business units to access relevant insights independently. Furthermore, incorporating MLOps practices will facilitate rapid integration and deployment of machine learning models for predictive analytics. The successful completion of this project will result in enhanced data observability and increased operational efficiency, providing a competitive edge in the market.