Diagnose zero IE uploads with SQL

On 2025‑09‑01 ('today'), the dashboard shows image uploads for Internet Explorer (IE) dropped to zero. Write SQL to (a) determine if this is a technical issue vs. behavior change, and (b) localize the fault (client, storage, ETL, or dashboard query). Use the schema below and produce: 1) A 8‑day trend by browser with significance-ready confidence intervals (approximate via normal or Wilson on proportions). 2) A client→storage funnel by browser for 2025‑09‑01 and the 7 days prior. 3) Top IE error codes and their first‑seen timestamps on 2025‑09‑01 vs. baseline. 4) A validation that the dashboard aggregation matches a direct recompute query; if they differ, show a minimal reproducible case. Finally, explain how your findings distinguish user‑behavior changes from instrumentation or storage bugs. Schema and tiny samples: Table: events_uploads Columns: event_time TIMESTAMP, user_id BIGINT, browser STRING, os STRING, app_version STRING, country STRING, status STRING, error_code STRING, ip STRING, session_id STRING, event_id STRING Sample: +---------------------+---------+---------+---------+------------+---------+---------+------------+-----------+-----------+----------+ | event_time | user_id | browser | os | app_version| country | status | error_code | ip | session_id| event_id | +---------------------+---------+---------+---------+------------+---------+---------+------------+-----------+-----------+----------+ | 2025-08-31 22:10:00 | 101 | IE | Windows | 11.2.0 | AR | success | NULL | 1.1.1.1 | s1 | e1 | | 2025-09-01 09:00:00 | 102 | IE | Windows | 11.2.1 | US | fail | UPLOAD_415 | 2.2.2.2 | s2 | e2 | | 2025-09-01 09:05:00 | 103 | IE | Windows | 11.2.1 | US | fail | UPLOAD_415 | 2.2.2.3 | s3 | e3 | | 2025-09-01 09:07:00 | 104 | Chrome | Windows | 120.0 | US | success | NULL | 3.3.3.3 | s4 | e4 | | 2025-09-01 10:20:00 | 105 | Chrome | Mac | 120.0 | US | success | NULL | 4.4.4.4 | s5 | e5 | | 2025-09-01 11:00:00 | 106 | IE | Windows | 11.2.1 | US | fail | UPLOAD_415 | 2.2.2.4 | s6 | e6 | +---------------------+---------+---------+---------+------------+---------+---------+------------+-----------+-----------+----------+ Table: storage_uploads Columns: upload_time TIMESTAMP, user_id BIGINT, object_id STRING, source_event_id STRING, browser STRING Sample: +---------------------+---------+-----------+----------------+---------+ | upload_time | user_id | object_id | source_event_id| browser | +---------------------+---------+-----------+----------------+---------+ | 2025-08-31 22:10:05 | 101 | objA | e1 | IE | | 2025-09-01 09:07:10 | 104 | objB | e4 | Chrome | | 2025-09-01 10:20:12 | 105 | objC | e5 | Chrome | +---------------------+---------+-----------+----------------+---------+ Table: dash_metrics Columns: metric_date DATE, browser STRING, uploads_count BIGINT Sample: +-------------+---------+---------------+ | metric_date | browser | uploads_count | +-------------+---------+---------------+ | 2025-09-01 | IE | 0 | | 2025-09-01 | Chrome | 2 | +-------------+---------+---------------+ Include at least: (i) a 7‑day trailing comparison query, (ii) a client‑to‑storage left join to quantify drop‑off by browser and error_code, and (iii) a dashboard-recompute query. Explain how each result points to either a client bug, storage failure, ETL/typing bug (e.g., IE mislabeled), or a dashboard filter error.