Real-Time Data Pipeline Optimization for Enhanced Content Analytics

As a dynamic player in the Media & Entertainment industry, our company is committed to delivering personalized and engaging content to our rapidly growing audience. However, the current data processing and analytics infrastructure struggles with latency issues and lacks the scalability required to support our expanding operations. We intend to overhaul our data pipeline architecture by implementing a robust real-time processing system using Apache Kafka for event streaming and Spark for data processing. In addition to these foundational technologies, integrating MLOps practices will enhance our ability to deploy and monitor machine learning models in production, ensuring high relevance of the content we recommend. The project will also involve setting up data observability frameworks to detect and rectify anomalies in data flow proactively, ensuring data quality and reliability. The freelancer will work closely with our internal data team to transition from our current monolithic architecture to a more flexible and scalable data mesh infrastructure. This transformation is crucial for maintaining our competitive edge in the rapidly evolving media landscape.