Real-time Data Engineering System for Operational Efficiency

Our SME in the Business Process Outsourcing industry seeks to revolutionize its data processing capabilities by implementing a real-time data engineering system. Our current data infrastructure struggles with latency issues, hindering timely insights and rapid decision-making. We aim to integrate technologies such as Apache Kafka for event streaming, Spark for data processing, and Snowflake for secure and scalable data storage. This solution will enable seamless data mesh frameworks, facilitating decentralized data ownership and improving collaboration across departments. Additionally, MLOps practices will be applied to automate the deployment of machine learning models, ensuring they efficiently leverage real-time data. With this project, we aim to elevate our data observability to proactively monitor and address data quality issues. The desired outcome is a robust infrastructure that supports real-time analytics, ultimately reducing operational costs and enhancing service delivery.