Real-Time Listener Analytics & Insights Platform for Podcast & Radio

Our enterprise Podcast & Radio company seeks to develop a state-of-the-art analytics platform that provides real-time insights into listener behaviors and preferences. The goal is to enhance content personalization, optimize ad placements, and drive higher engagement. We aim to build a robust data infrastructure employing technologies such as Apache Kafka for event streaming and Spark for large-scale data processing. The platform will integrate with our existing data ecosystem, utilizing Airflow for workflow management, dbt for data transformation, and Snowflake as our cloud data warehouse. By incorporating data mesh principles, we intend to decentralize data ownership and promote interdepartmental collaboration. The project will also explore MLOps practices to deploy machine learning models that predict listener trends and personalize content offerings. This initiative is critical in maintaining our competitive edge and meeting the increasing demand for personalized and real-time content experiences.