Integrated Data Pipeline for Enhanced Community Engagement

We are a mid-sized religious organization looking to improve our data infrastructure to better engage with our community and enhance our outreach activities. Currently, our data is fragmented across various platforms, making it challenging to gain actionable insights. We want to leverage real-time analytics and data mesh architecture to create a unified, robust data ecosystem. The project involves setting up a data pipeline using technologies like Apache Kafka for event streaming, Apache Spark for data processing, and Airflow for orchestrating data workflows. Additionally, we seek to implement MLOps practices to ensure continuous integration and delivery of machine learning models that provide insights into community preferences and engagement metrics. The output will be stored in Snowflake or BigQuery for easy access and analysis. This project is crucial for us to make informed decisions, tailor our programs to better suit our community's needs, and ultimately foster a stronger, more connected congregation.