From Ingestion to Action: Architecting Intelligent DataOps Pipelines for Real-Time Consumer Systems

Today’s focus on instant updates in systems for people means that traditional ways of working with data must evolve. The paper provides a detailed model for creating intelligent DataOps pipelines that help move data from ingestion to live decision-making. Using this architecture, data enters the system in streams, is checked for quality automatically, is directed using metadata, gets analysed with the help of machine learning and benefits from continuous updates. Much focus is given to how well the system can handle growth, quick response times and works together with recommendation engines, systems for catching fraud and insights into user actions. The architecture uses microservices which makes it possible to easily deploy software in any hybrid cloud environment. Experiments have revealed that processing time, the system’s response and prediction accuracy have all improved a lot. This work introduces a new DataOps way of working that combines data engineering, analytics and operational flexibility to make intelligent decisions in real time for consumer systems.