Advanced SQL

Chapter 5. Building Data Engineering Solutions with SQL

Building a data engineering solution entirely with SQL as the primary language may sound ambitious, but modern platforms and tools make it close to achievable. In this chapter, we design and implement an end-to-end data pipeline where SQL is the primary language for defining data structures, transformations, and analytical semantics, while the surrounding platform components remain SQL-friendly. The goal is to show that SQL, coupled with the right ecosystem, can anchor everything from real-time data ingestion to building a data lakehouse, while following best practices for scalability and maintainability.

Some parts of the system, specifically streaming ingestion engines (such as Spark Structured Streaming), orchestration services, and storage maintenance processes like compaction and snapshot management, involve system-level concerns beyond pure SQL syntax. Some of these components are used in practical form throughout the chapter, but the focus remains on how they integrate with SQL-based modeling layers rather than on the low-level operational engineering required to run them at scale. The goal is not to turn the reader into a platform engineer but to show how SQL-driven layers integrate into a broader data platform where surrounding systems are designed to remain SQL-compatible rather than SQL-exclusive.

Our scenario is a simple retail example with three core tables: Customers, Products, and Orders. Customers purchase ...