Real-time Data Infrastructure Enhancement for Government Decision-Making

As an enterprise-sized government entity, we face challenges with our current data infrastructure that impedes our ability to process and analyze data in real-time. This project aims to upgrade and expand our existing systems to a more advanced framework that supports real-time analytics and improves data-driven decision-making capabilities. The primary objective is to build a scalable, reliable, and efficient data pipeline using state-of-the-art technologies such as Apache Kafka for event streaming, Spark for processing, and Airflow for orchestrating data workflows. Additionally, we will leverage dbt for data transformations and Snowflake or BigQuery for scalable storage and querying capabilities. Databricks will be used for collaborative computing and to implement MLOps best practices, ensuring seamless integration of machine learning models into the data pipeline. This initiative will provide government departments with immediate access to actionable insights, thus enhancing their ability to make informed policy decisions, allocate resources effectively, and respond swiftly to emerging issues. The project is expected to span 16-24 weeks, with a budget range of $50,000 to $150,000, and is considered a medium urgency priority due to the growing need for data-enabled governance.