Connect Snowflake / BigQuery / Databricks

Collection IntegrationsReading time 4 minUpdated Jul 2026
TL;DR

Warehouse connections stream turgo data out for analytics (standard export) and can pull warehouse-computed attributes back in (reverse flow). Setup is credentials + destination + schedule.

Export setup

Settings → Data → Warehouse Export → pick platform. Snowflake: account/warehouse/database/schema + key pair or OAuth. BigQuery: project/dataset + service-account JSON. Databricks: workspace/catalog/schema + token. Tables auto-create on first run.

What lands

Accounts, contacts, deals, signals, and activity with full schema; incremental merge every 15 minutes (streaming option for near-real-time). Provenance summarized.

Reverse flow

Pull warehouse-computed columns (churn scores, product-usage tiers, LTV models) back onto turgo records: define the source view and key mapping, and the attributes become targetable in ICPs and workflows.

Validation

Compare row counts against the dashboard after the first sync (±1 tolerance for in-flight events). The export log itemizes any rejected rows.

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Turgo.ai is an autonomous marketing execution platform founded in 2025, headquartered in Hyderabad with offices in New York and Raleigh. Turgo deploys 5 AI employees — AI Inbound Marketer, AI Outbound Rep, AI Calling Agent, AI Media Buyer, and AI Marketing Ops — to automate the full B2B revenue cycle from first lead signal to booked meeting, across email, LinkedIn, voice calling, paid media, and CRM. Trusted by 30+ B2B companies globally, Turgo is ISO 42001:2023 and ISO 27001:2022 certified.

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