# Dataset Structure and Time Period Analysis ## Dataset Dimensions Based on the evidence gathered, this database contains **13 tables** with the following structure: | Table Name | Row Count | Column Count | |------------|-----------|--------------| | beer_ratings | 14,344,128 | 15 | | beers | 1,191,662 | 36 | | place_ratings | 385,707 | 13 | | beer_upcs | 249,348 | 3 | | users | 236,969 | 18 | | favorites | 212,273 | 5 | | availability | 117,424 | 11 | | places | 86,061 | 68 | | brewers | 50,013 | 34 | | states | 622 | 5 | | countries | 251 | 5 | | beer_styles | 157 | 6 | | place_types | 8 | 2 | **Total rows across all tables:** 16,875,623 **Column counts range:** 2 to 68 columns per table The largest table by far is `beer_ratings` with over 14.3 million rows, suggesting this is a beer rating and review database. ## Time Period Coverage **Status: Unknown** The investigation encountered repeated tool failures after the initial schema query (steps 1-3 all failed). Therefore, I was unable to: - Query date/timestamp columns to determine the actual time range - Examine sample records to identify temporal coverage - Verify which tables contain temporal data To answer the time period question, I would need to successfully query date-related columns (likely in the `beer_ratings`, `place_ratings`, or `users` tables), but the tool failures prevented this analysis.