Real-time Data Pipeline Implementation for Influencer Engagement Analytics

In the rapidly evolving Social Media & Influencer industry, understanding real-time engagement metrics is critical for maintaining competitive advantage and delivering personalized user experiences. Our company is looking to implement a cutting-edge data engineering solution to process and analyze influencer engagement data in real-time. We aim to leverage technologies such as Apache Kafka for event streaming, Spark for large-scale data processing, and Airflow for orchestrating complex data workflows. Using dbt and Snowflake will facilitate efficient data transformation and storage, while BigQuery and Databricks will be employed for advanced analytics. The project will focus on creating a data mesh architecture to decentralize data ownership, ensuring high data quality and availability. Key deliverables will include the setup of a real-time analytics dashboard for instant insights, data observability tools to monitor pipeline health, and MLOps integration for continuous model development and deployment. This initiative will provide our marketing and influencer management teams with the tools to optimize influencer partnerships and improve campaign effectiveness.