Add src/remote_query.py CLI module enabling the AI agent to run SQL
queries spanning local Parquet tables and remote BigQuery tables in a
single DuckDB session on the server. Two-phase protocol: BQ sub-queries
(--register-bq) fetch filtered/aggregated data, then DuckDB SQL (--sql)
joins everything.
Safety: COUNT(*) pre-check, memory estimation (2GB cap), row limits
(500K per BQ sub-query, 100K final result).
Changes:
- New src/remote_query.py with CLI, BQ registration, output formatting
- Add bq_entity_type field to TableConfig (view vs table routing)
- Extract create_local_views() from duckdb_manager.py for reuse
- Update claude_md_template.txt with remote query agent instructions
- Update example configs with remote_query section and docs
- 52 new tests (42 remote_query + 10 bq_entity_type), all passing
- New sync_schedule and profile_after_sync fields in TableConfig
(formats: "every 15m", "every 1h", "daily 05:00")
- New src/scheduler.py with schedule evaluation logic (is_table_due)
- New --scheduled mode in data_sync.py: only syncs tables that are due,
respects profile_after_sync flag, auto-restarts webapp after profiling
- Systemd timer+service for data-refresh (every 15 min)
- Systemd timer+service for catalog-refresh (every 15 min)
- deploy.sh enables new timers automatically
- Complete table config reference in data_description.md.example
- 58 new scheduler tests
Open-source AI data analyst platform extracted from internal repo.
Includes data sync engine, Keboola adapter, Flask web portal,
server deployment scripts, and configuration templates.
