Real-time Data Integration and Analytics Implementation for Restaurant Chain

As a leading enterprise in the Restaurants & Food Service industry, we recognize the critical importance of data-driven decision-making. Our current data systems are siloed, causing delays in accessing vital business metrics which hampers our ability to respond quickly to market demands and customer preferences. This project involves designing and deploying a robust data architecture that integrates and analyzes data from various touchpoints in real-time. Using Apache Kafka for event streaming, Spark for data processing, and Databricks for collaborative analytics, we aim to establish a data mesh that democratizes data access across the organization. Additionally, dbt will be used for data transformation, and Snowflake or BigQuery for data warehousing. By enhancing data observability, we strive to reduce downtime and improve operational efficiency. This initiative is aligned with our strategic goal of enhancing customer experiences through personalized services, optimizing supply chain operations, and implementing dynamic pricing models based on real-time demand forecasting.