Enhancing Influencer Engagement with Real-Time Data Analytics Platform

The project involves designing and implementing a real-time data analytics platform tailored for the Social Media & Influencer industry. Our current challenge is the inability to quickly process and analyze vast amounts of engagement data across multiple social media platforms. This limitation hampers our ability to understand influencer performance and engagement metrics in a timely manner. The proposed solution will integrate key technologies such as Apache Kafka for event streaming, Apache Spark for processing, and Snowflake for data warehousing. The platform will employ Airflow for orchestrating data pipelines and dbt for data transformation. With Databricks, we will facilitate advanced analytics and machine learning capabilities. The platform will provide comprehensive dashboards with real-time analytics that enable marketers to track influencer performance, engagement rates, and campaign effectiveness. By implementing a data mesh architecture, we ensure data accessibility across different teams, promoting a decentralized data management approach. The project aims to improve the speed and accuracy of decision-making processes, thereby increasing the efficacy of influencer marketing strategies.