Subqueries and Common Table Expressions (CTEs) are both mechanisms for breaking a complex query into composable, named parts. Without them, multi-step analytical logic must either be expressed as a single deeply nested SQL expression — which is unreadable and difficult to debug — or split across multiple intermediate tables or views. Subqueries embed one SELECT statement inside another, while CTEs define named temporary result sets using a WITH clause before the main query, making the logic readable and step-by-step.
The choice between subqueries and CTEs is not merely stylistic — it affects readability, debuggability, and in some databases, performance. CTEs are particularly powerful in data engineering because they allow complex multi-step transformations to be expressed in a single SQL statement that reads like a sequential narrative: define the source, filter it, aggregate it, rank it, then select the top results. This readability directly reduces the review and maintenance cost of pipeline transformation queries.
Recursive CTEs extend this further, enabling hierarchical queries — queries that traverse tree or graph structures — which are not possible with standard SELECT-FROM-WHERE syntax. For data engineers working with organisational hierarchies, category trees, or network graphs stored in relational tables, recursive CTEs are the correct and often the only SQL-native tool for the job. Understanding when to use each form — inline subquery, scalar subquery, CTE, or recursive CTE — is a key skill in producing maintainable transformation code.