BigQuery
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BigQuery serves as your primary repository for high-scale behavioral truth, while Planhat acts as your system of commercial execution. By establishing a direct pipeline between the two, teams transform raw warehouse rows into actionable customer context, powering objective Health Scores and automated follow-through to drive retention and growth.
Unlock the power of BigQuery with planhat
Improve net revenue retention
Protect recurring revenue by identifying risk and opportunity signals before they manifest as churn. Planhat ingests BigQuery time-series usage data, allowing commercial teams to layer customer context on top of raw event logs to catch early friction.
Shorten time to value
Accelerate implementation by syncing technical milestones directly from your warehouse. Mapping BigQuery tables to Planhat models removes manual status updates, ensuring implementation teams work from a complete, synchronized dataset from day one.
Increase process governance
Maintain strict data integrity without custom engineering dependencies. Planhat uses distinct key rules—unique text IDs for CRM and numeric incrementing keys for usage rows—to fetch only new records, ensuring data pipelines remain reliable and duplicate-free.
Improve commercial predictability
Base renewal forecasts on objective adoption rather than anecdotal sentiment. By combining BigQuery revenue objects with live product engagement signals, leadership maintains a reliable, real-time view of expansion potential and portfolio health.
how it works
Flow & configuration
Secure authentication via App Center
Authorized users connect the BigQuery application within the App Center by establishing an OAuth-based connection or, for specific enterprise security needs, dataset-scoped service accounts. Users copy the Planhat redirect URI into the Google Cloud Console to authorize the secure data bridge and select the target database.
Define mapping for CRM and usage data
Create sync sections that link specific BigQuery tables or views to Planhat data models. Static CRM data—such as Companies, End Users, and Licenses—can be configured for bidirectional, send-only, or receive-only sync to ensure both systems remain aligned.
Configure unique incrementing sync keys
To ingest time-series usage data, administrators must specify a unique, numeric incrementing column—such as a serial ID or timestamp—that Planhat uses to track which rows have already been synced. This mechanism ensures that high-volume event streams land in Planhat incrementally without creating duplicates or impacting warehouse performance.
Map event signals to customer records
Link BigQuery usage rows to the correct Planhat model, including Companies, End Users, Assets, or Projects. These land in Planhat as granular User Activities or aggregated Custom Metrics, providing the foundation for real-time behavioral analysis and health scoring.
Establish automated sync cadence
Set the execution frequency for usage data, with options ranging from every five minutes to daily batches. CRM records are typically updated on an hourly schedule when Automation is enabled, ensuring that account context and implementation progress stay current across the commercial organization.
How secure is the connection between BigQuery and Planhat?
How do you map BigQuery's nested data structures to Planhat's customer model?
How does Planhat minimize BigQuery scan costs during data syncing?
How does Planhat operationalize raw BigQuery records into system of action workflows?
Why sync BigQuery to Planhat instead of relying on Google Looker or other BI tools?
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