Real-Time Data Pipeline Optimization for Event Analytics

In the dynamic catering and events industry, timely and accurate data insights are crucial for maintaining competitive advantage and ensuring customer satisfaction. Our company aims to overhaul its existing data architecture to facilitate real-time analytics across various functions including event logistics, customer engagement, and feedback analysis. The current data infrastructure lacks the capacity to handle diverse data streams efficiently, resulting in delayed insights and decision-making. We are looking to build a scalable and flexible data pipeline using Apache Kafka for event streaming, Spark for real-time analytics, and Airflow for orchestrating complex data workflows. By integrating dbt and Snowflake, we aim to enhance our data transformation and warehousing capabilities, enabling us to generate actionable insights promptly. This project will also incorporate data observability practices to ensure data quality and reliability. The successful implementation of this solution will empower our team to make data-driven decisions rapidly, improve customer experiences by personalizing services, and streamline operational processes. We expect the project to be completed within 8-12 weeks, with a budget allocation of $25,000 - $75,000.