Went through the basics fine but I rambled a bit when they pushed on real-world use cases.
Start by defining stored procedures clearly, then pivot to their role in data engineering pipelines, emphasizing trade-offs between performance, maintainability, and portability. Use a concrete example from your experience to illustrate when and why you'd choose a stored procedure over application-layer logic.
Pro tip: At Google-scale, stored procedures are often avoided due to vendor lock-in and testing challenges, so acknowledge their limitations and suggest alternatives like dbt or orchestration tools when appropriate. Show that you evaluate technology choices based on team needs and long-term maintainability, not just convenience.
Explain that stored procedures are precompiled SQL scripts stored in the database, executed on demand, and can include control flow, parameters, and error handling.
Highlight scenarios like ETL/ELT transformations, data validation, scheduled batch jobs, and encapsulating complex business logic close to the data.
Compare stored procedures with application-side SQL or orchestration tools: performance gains vs. vendor lock-in, debugging difficulty, version control, and testing.
Walk through a real or hypothetical pipeline where a stored procedure was used for incremental aggregation or data cleansing, and explain the decision criteria.
Summarize when to use them (e.g., high-volume transformations, legacy systems) and when to avoid them (e.g., cloud-native, cross-database environments).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.