phase 14-15: docker build validation and helm deployment
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Analytical fact table definitions for MinIO-backed datasets queried via Trino.
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All tables use Hive-compatible partition layouts on MinIO (`s3a://stonks-lakehouse/warehouse/`)
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and are defined in the `lakehouse.stonks` schema. Parquet is the storage format.
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## Fact Tables
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- `lake.market_bars` — OHLCV bar data
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- `lake.market_quotes` — quote snapshots
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- `lake.company_events` — corporate actions and events
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- `lake.documents` — ingested document metadata
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- `lake.document_extractions` — AI extraction outputs
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- `lake.trade_signals` — aggregated trend signals
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- `lake.trade_orders` — order submission records
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- `lake.trade_fills` — fill and execution records
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- `lake.market_bars` — OHLCV bar data per symbol per interval
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- `lake.market_quotes` — bid/ask quote snapshots
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- `lake.company_events` — corporate actions, earnings, filings, and issuer events
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- `lake.documents` — ingested document metadata (articles, filings, transcripts)
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- `lake.document_extractions` — AI extraction outputs per document per company
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- `lake.trade_signals` — aggregated trend signals and recommendation actions
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- `lake.trade_orders` — order submission records (paper and live)
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- `lake.trade_fills` — fill and execution records from broker
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- `lake.positions_daily` — end-of-day position snapshots
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- `lake.pnl_daily` — daily PnL records
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- `lake.pnl_daily` — daily PnL records per symbol per account
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- `lake.prediction_vs_outcome` — prediction accuracy tracking
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- `lake.model_performance` — extraction model performance metrics
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## Partitioning
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- Most tables partition by `dt` (date)
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- `document_extractions`, `prediction_vs_outcome`, and `model_performance` also partition by `model_version`
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## Trino Catalogs
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- `lakehouse` catalog (Hive connector) for external Hive-compatible tables
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- `iceberg` catalog (Iceberg connector) for managed Iceberg tables
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## Views
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Example SQL views for dashboards and ad hoc analysis are in `lakehouse/views/`.
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See `lakehouse/views/README.md` for details.
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