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?
Planhat provides a secure, native connector for Google BigQuery designed for enterprise-grade governance. Configuration via Google Cloud Service Accounts with scoped IAM Roles. A JSON Service Account Key with Read-only access to specific datasets ensures Planhat ingests telemetry and commercial data via encrypted channels. Zero-write-back policy maintains total data integrity — BigQuery remains the single source of truth.
How do you map BigQuery's nested data structures to Planhat's customer model?
BigQuery often stores complex nested and repeated fields (JSON/Structs). Planhat supports Custom SQL or Materialized Views within BigQuery to flatten these structures before ingestion — joining product events, billing history, and user profiles, then mapping resulting columns to Planhat Attributes, Events, or Assets. Primary Keys such as account_id map cleanly, creating a unified Golden Thread.
How does Planhat minimize BigQuery scan costs during data syncing?
Planhat utilizes Partition-Aware Incremental Syncing to optimize Google Cloud bills. Instead of full table scans that process unnecessary data volumes, Planhat fetches only new or modified rows based on Partitioned Tables — by timestamp or last_modified. Bytes Scanned reduce significantly, enabling high-performance, cost-effective syncs with more frequent data refreshes.
How does Planhat operationalize raw BigQuery records into system of action workflows?
Planhat acts as the activation layer for Google Cloud data. Raw BigQuery records — query volume, feature usage — transform into Operational Intelligence. When a specific threshold is met in warehouse data, Planhat automatically triggers an Onboarding Playbook, assigns a task to a Customer Success Manager, or fires a Churn Risk Alert. Cold storage becomes a proactive system of action.
Why sync BigQuery to Planhat instead of relying on Google Looker or other BI tools?
BI tools like Google Looker are designed for Visualizing Historical Trends. Planhat is designed for Executing Real-time Responses. Looker shows a dashboard of declining product adoption. It cannot automatically launch a customer success plan or send a personalized re-engagement email. Syncing BigQuery to Planhat bridges Passive Analytics and Active Intervention — warehouse insights become actionable in the daily operational workspace.
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