## Day 2 12:00 — Data quality dashboard for Omar
I reviewed the task for the data quality dashboard and decided to build it as a simple, read-only view rather than a complex interactive tool. The core requirements are row counts, drift checks, and last successful sync per customer, all of which can be derived from existing sync metadata tables. I drafted the schema for the `sync_metrics` table to store these aggregates efficiently, ensuring we can query the latest state without expensive joins on every refresh. I also started the Rust struct definitions for the API response to keep the backend simple and typed. I will coordinate with Sofia on the auto-refresh interval to ensure it doesn't overload the database.

## Day 2 13:30 — Data quality dashboard for Omar
I'm not building a dashboard; I'm building a health check. A dashboard is a UI problem; this is a data integrity problem. If a customer's row count drops by 10%, that's not a "drift" to visualize, that's a data loss incident. I'm writing the SQL view that Omar will query. It calculates the delta from the last known good state. If the delta is non-zero, it's red. No fancy charts. No auto-refresh logic in the SQL; that's Elena's job in React. The simplest thing that could work is a `SELECT` that returns the last sync timestamp, the expected row count, and the actual row count. If they don't match, we have a problem. I'm also adding a comment for Aiko: do not use `COUNT(*)` on the main table for the "expected" count; use the metadata table we populated during the initial migration. Counting 40 million rows to check if it's 40 million is a waste of CPU.

## Day 4 14:00 — Data quality dashboard for Omar
Built the data quality dashboard backend and frontend. Created a Rust endpoint that queries Postgres for row counts, drift metrics, and last sync timestamps per customer, returning a JSON summary. Wrote a TypeScript React component that fetches this data every 30 seconds and displays it in a simple table. Added a Postgres migration to create a lightweight stats table for drift tracking. Kept it boring: no charts, no animations, just numbers that update. If a sync fails, the last successful sync timestamp simply doesn't change.

## Day 4 14:30 — Data quality dashboard for Omar
I reviewed T8 and decided the simplest thing that could work is a read-only SQL view plus a lightweight TypeScript/React page that polls it every thirty seconds. No new tables, no complex aggregation logic, just counts and timestamps. I wrote the migration and the dashboard component. Ravi should add the snapshot tests for the drift thresholds before we ship this to a pilot hospital, and Sofia needs to confirm the auto-refresh interval doesn't spike CPU on the on-prem nodes. I did not add any write path here; this is strictly observational.

## Day 5 13:00 — Data quality dashboard for Omar
For T8, I'm handing Omar the Postgres migration and a minimal React component for the data quality dashboard. I'm intentionally keeping it boring: no fancy charting, just three numbers per customer—row count, last sync timestamp, and a drift flag. I'm leaning on a view in Postgres to precompute the drift check so the frontend doesn't have to do math. I asked Omar to confirm with Sofia that the view will run on the primary DB, not a replica, because we cannot risk stale data in a hospital context. This is the simplest thing that could work; if it breaks, we look at the view, not the React layer.