Database Engineering Fundamentals: Architecture and Scaling

Choosing the right database and configuring it for high performance requires more than just knowing SQL queries. To build resilient, scalable applications, you must understand the underlying engineering principles that govern how data is stored, indexed, and retrieved. This course guides you through the core architectural patterns of modern database systems. You will transition from simply writing queries to understanding database internals, enabling you to make optimal trade-offs between performance, consistency, and scale. What you'll learn: - Understand the core mechanics of storage engines, transaction processing, and the ACID properties that guarantee data integrity. - Analyze indexing strategies, including B-Trees and LSM-Trees, to optimize query performance and read/write paths. - Design scalable database architectures using partitioning, sharding, and replication techniques. - Manage database concurrency and isolation levels to prevent data corruption in high-traffic systems. - Evaluate trade-offs between relational, non-relational, and modern vector databases designed for AI applications. You will begin by exploring fundamental storage mechanics and transaction concepts before moving on to advanced topic areas like distributed systems and modern database engines. Through written explanations and structural breakdowns, you will learn how to evaluate and select the right database technology for any system architecture. This course is designed for software developers, system architects, and aspiring database engineers who want to understand database internals. No prior database administration experience is required, as we start with foundational terminology and build up to complex distributed concepts. Start building a stronger foundation in backend systems engineering today.