Real-Time Data Pipeline Optimization for Enhanced Broadcast Analytics

As a burgeoning broadcasting startup, we are focused on delivering engaging content to our viewers. However, our current data processing infrastructure isn't meeting the demands for real-time analytics. We need a robust and efficient data pipeline that processes and analyzes streaming data to derive actionable insights quickly. The project involves designing and optimizing a data pipeline using Apache Kafka for event streaming, Spark for data processing, and Airflow for workflow management. The integration with cloud-based data warehouses like Snowflake or BigQuery is crucial for storage and advanced analytics. The ideal candidate will also implement data observability techniques to ensure data quality and reliability throughout the pipeline. This improvement will enable our team to make data-driven decisions faster, enhancing viewer engagement and content relevance.