Real-Time Data Pipeline for Enhanced Audience Insights

As a dynamic player in the News & Journalism industry, we understand the critical importance of staying connected with our audience's preferences and behaviors. To achieve this, we require a robust data engineering solution capable of delivering real-time insights. This project involves designing and implementing a data pipeline using tools such as Apache Kafka for event streaming, and Snowflake for scalable data warehousing. The pipeline will collect data from various sources, including website analytics, social media interactions, and publication metrics, process it through Apache Spark for transformation, and store it for analysis in Snowflake. The implementation of Airflow will ensure the orchestration of data workflows, while dbt will handle data transformations. Additionally, integrating Databricks will allow us to apply MLOps practices, ensuring the model's continuous integration and deployment for predictive analytics. The outcome will empower our editorial and marketing teams with real-time data-driven insights, enhancing decision-making processes and ultimately improving audience engagement and content relevance.