Data Engineering Foundations with Real-Time Pipelines

Modern organizations rely on clean, reliable, and timely data to drive decisions and power intelligent applications. This text-based course guides you from the absolute basics of data architecture to building resilient pipelines that process data in real time. You will understand how to structure modern data systems, handle big data workloads, and manage cloud storage efficiently. By the end of this course, you will be able to design robust data pipelines, orchestrate workflows, and manage both batch and streaming data architectures. What you'll learn: Understand core data engineering concepts, storage paradigms, and database types; Design scalable data lakes and storage solutions on AWS; Build real-time streaming pipelines using Kafka and Flink; Process large-scale datasets and machine learning workflows with Spark; Apply modern infrastructure-as-code principles and basic CI/CD to data pipelines; Orchestrate complex data workflows with modern scheduling tools. We start with foundational definitions, data modeling concepts, and cloud storage basics before moving into hands-on pipeline construction and real-time stream processing. This course is designed for beginners, aspiring data professionals, and software developers looking to transition into data engineering, with no prior big data experience required. Start reading today to build the infrastructure that powers modern data-driven applications.