Engineering enterprise data infrastructure: Architecting scalable pipelines, APIs, machine learning systems, and cloud-native deployment frameworks

This comprehensive guide explores the integrated landscape of modern data engineering and machine learning technologies. The article examines the foundational components of data infrastructure, beginning with data pipelines that transform raw information into valuable insights through Apache Spark and Hadoop, while highlighting how these pipelines increasingly incorporate ML workflows for feature engineering and model training. It investigates how applications communicate through REST and GraphQL APIs, with special attention to model serving interfaces and feature access patterns. The discussion compares structured SQL databases with flexible NoSQL solutions and vector databases optimized for AI workloads, then introduces orchestration tools such as Airflow and specialized ML frameworks for managing complex workflows. This article extends to continuous integration and deployment practices for machine learning systems, concluding with containerization strategies through Docker and Kubernetes that enable scalable deployment of both traditional applications and sophisticated machine learning models. By breaking down these sophisticated concepts into accessible explanations, readers will gain practical knowledge applicable to building modern data and ML infrastructures.