VectorFin/Integrations/BigQuery

VectorFin

Connect BigQuery to VectorFin Iceberg Data

Query VectorFin Iceberg tables directly from BigQuery with external table registration — no ETL, no copies.

5 min

Setup time

Iceberg tables

5K+

US tickers

Nightly

Updates

Prerequisites

📋VectorFin Pro plan

🔑API key from app.vectorfinancials.com

☁️BigQuery account

Connection Guide

Get API access and service account

Sign up at app.vectorfinancials.com (Pro tier required). Create a BigQuery Connection with your GCS service account that has read access to gs://vectorfinancials-data/.

bash

# Create BigQuery connection to GCS
bq mk --connection \
  --connection_type=CLOUD_RESOURCE \
  --location=US \
  your-project:US.vf_connection

# Grant storage access to the connection service account
gsutil iam ch serviceAccount:SERVICE_ACCOUNT@developer.gserviceaccount.com:roles/storage.objectViewer \
  gs://vectorfinancials-data

Register external Iceberg table

Create external tables pointing to VectorFin's GCS Iceberg warehouse. Run once; re-run after each nightly pipeline update to refresh metadata.

sql

-- Register transcript embeddings table
CREATE OR REPLACE EXTERNAL TABLE `your-project.your_dataset.vf_transcripts`
WITH CONNECTION `us-central1.vf_connection`
OPTIONS (
  format = 'ICEBERG',
  uris = ['gs://vectorfinancials-data/warehouse/embeddings/transcripts/metadata/LATEST.metadata.json']
);

-- Register whystock score signals table
CREATE OR REPLACE EXTERNAL TABLE `your-project.your_dataset.vf_whystock_score`
WITH CONNECTION `us-central1.vf_connection`
OPTIONS (
  format = 'ICEBERG',
  uris = ['gs://vectorfinancials-data/warehouse/signals/whystock_score/metadata/LATEST.metadata.json']
);

Verify with a count query

Run a simple COUNT to confirm the table is accessible and data is flowing.

sql

SELECT COUNT(*) as total_rows, COUNT(DISTINCT ticker) as tickers
FROM `your-project.your_dataset.vf_transcripts`;
-- Expected: ~500K rows, 5000+ tickers

Query embeddings and signals

Run your first real query — semantic embeddings for AAPL and a bitemporal point-in-time signal lookup.

sql

-- AAPL semantic embeddings for 2024
SELECT ticker, fiscal_period, chunk_idx, embedding
FROM `your-project.your_dataset.vf_transcripts`
WHERE ticker = 'AAPL' AND fiscal_period LIKE '2024%'
ORDER BY fiscal_period, chunk_idx;

-- Bitemporal point-in-time query (as-of Jan 1 2024)
SELECT ticker, date, score, components
FROM `your-project.your_dataset.vf_whystock_score`
WHERE ticker = 'AAPL'
  AND knowledge_ts <= TIMESTAMP('2024-01-01')
ORDER BY effective_ts DESC
LIMIT 1;

Available Tables

All 7 VectorFin data tables — bitemporal (effective_ts + knowledge_ts), append-only, nightly updates.

embeddings/transcriptsEarnings call chunk embeddings (768-dim)

▼

sql

SELECT ticker, fiscal_period, chunk_idx, embedding FROM vf_transcripts WHERE ticker = 'NVDA' LIMIT 10

embeddings/filingsSEC filing section embeddings

▼

sql

SELECT ticker, filing_type, section, embedding FROM vf_filings WHERE ticker = 'MSFT' AND filing_type = '10-K'

signals/whystock_scoreComposite quant score (0–100)

▼

sql

SELECT ticker, date, score FROM vf_whystock_score WHERE date >= '2024-01-01' ORDER BY score DESC LIMIT 20

signals/regimeMarket regime (trending/ranging/volatile)

▼

sql

SELECT ticker, date, regime, confidence FROM vf_regime WHERE ticker = 'AAPL'

signals/volatilityGARCH volatility forecasts (1d/5d/21d)

▼

sql

SELECT ticker, date, garch_vol_1d, garch_vol_21d FROM vf_volatility WHERE ticker = 'TSLA'

signals/sentiment_driftEarnings sentiment drift vectors

▼

sql

SELECT ticker, fiscal_period, drift_vector FROM vf_sentiment_drift WHERE ticker = 'AMZN'

signals/anomalyAnomaly scores and flags

▼

sql

SELECT ticker, date, anomaly_score, flags FROM vf_anomaly WHERE anomaly_score > 0.8

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