Big Data Software Architecture Fundamentals

Addressing the challenge of scaling software to meet the demands of modern data growth requires more than just faster hardware. This course provides a comprehensive introduction to the principles of architecting systems that can ingest, process, and store data at an immense scale. You will move from a basic understanding of software design to a structured approach for building high-performance data environments. You will gain the skills to evaluate and select the right technologies for specific data challenges, ensuring your systems remain stable and responsive as they grow. What you'll learn: - Understand foundational distributed systems concepts, including the CAP theorem and data consistency models. - Apply architectural patterns such as Lambda and Kappa to balance batch and real-time processing needs. - Evaluate different storage technologies, from NoSQL databases to modern vector databases for AI applications. - Design robust data pipelines that ensure reliability and fault tolerance during high-volume ingestion. - Implement observability and monitoring patterns to maintain visibility into complex data flows. - Practice making architectural trade-offs between latency, throughput, and system complexity. Starting with essential terminology and the history of distributed computing, the material progresses through the structural components and decision-making frameworks used in modern data engineering. This course is designed for beginners in software engineering or data science who want to understand the structural side of big data; no prior architecture experience is required. Begin your journey into the world of large-scale system design.