Analyze Large Dataset Trends in SQL

Context

Excel can become slow and error-prone when working with datasets above 100,000 rows. In SQL-based analytics workflows, candidates are expected to explain how they would identify trends directly in the database instead of relying on spreadsheet tooling.

Question

Explain how you would analyze trends in a large dataset using SQL when the raw data is too large for comfortable analysis in Excel. Your answer should cover:

1. How to summarize large row-level data into trend-friendly outputs
2. Which SQL techniques you would use to group data over time
3. How filtering, aggregation, and ordering help reveal patterns
4. Why SQL is more appropriate than Excel at this scale

Scope Guidance

Keep the discussion focused on practical SQL analysis for large tables in PostgreSQL. The interviewer is looking for clear reasoning, common query patterns, and awareness of performance and data-quality considerations rather than advanced optimization details.