Real-Time Data Infrastructure for Municipal Service Optimization

As a rapidly growing municipal services provider, we face challenges in efficiently managing and optimizing our services across various departments, including waste management, public transportation, and emergency response. To address these challenges, we require a state-of-the-art real-time data infrastructure. The project will involve implementing an event streaming platform using Apache Kafka, integrating with Apache Spark for processing large datasets, and utilizing Airflow for data orchestration. Our existing data will be organized in a data mesh architecture to enable better data observability and collaboration among departments. The successful implementation will allow us to leverage real-time analytics for faster decision-making and predictive insights, ultimately leading to improved service delivery and resource allocation. Key deliverables include a scalable data pipeline, integration with Snowflake or BigQuery for cloud data warehousing, and setting up MLOps practices to streamline machine learning deployment. This initiative is critical to our growth strategy, demanding medium to high urgency and offering a budget of $15,000 to $50,000.