Building a Real-time Data Pipeline for Enhanced Donor Engagement

The Non-Profit organization aims to harness the power of data to deepen relationships with donors and maximize fundraising outcomes. Currently, donor data is scattered across multiple platforms, leading to delayed insights and missed opportunities for timely engagement. This project seeks to build a robust and scalable data pipeline capable of ingesting, processing, and analyzing donor data in real-time. Utilizing technologies such as Apache Kafka for event streaming, Apache Spark for scalable data processing, and Snowflake for data warehousing, the project will integrate disparate data sources into a unified platform. Additionally, Airflow will orchestrate the data workflows, while dbt will be used for data transformations. By enabling real-time analytics and dynamic donor segmentation, the organization can tailor its communication strategies, aligning outreach efforts with donor preferences and increasing engagement rates. The project will also implement data observability practices to ensure data quality and reliability. The implementation of MLOps will facilitate the deployment of machine learning models to predict donor behaviors, further enhancing strategic decision-making. This initiative is poised to transform the organization’s approach to donor engagement, driving significant improvements in fundraising performance.