Streamlined Data Infrastructure for Veterinary Clinics Using Real-Time Analytics

As a leading provider of veterinary services with a vast network of clinics, we are experiencing an exponential growth in data generated from our operations, patient records, inventory management, and customer interactions. To maintain our competitive edge, we need an efficient data infrastructure that leverages real-time analytics and event streaming. The project entails designing and deploying a scalable data architecture incorporating Apache Kafka for real-time data ingestion, Spark for processing, and Snowflake or BigQuery for storage and analytics. We also plan to utilize Airflow and dbt to orchestrate and manage data workflows, ensuring seamless data ops processes. This initiative will allow us to implement MLOps practices, improving predictive analytics capabilities for better resource allocation and service personalization. The successful implementation of this infrastructure will provide our team with actionable insights, ultimately enhancing patient care and operational efficiency.