Connectors / Integration
Connect Google BigQuery and Looker for Faster, Smarter Business Intelligence
Automate data pipelines between BigQuery and Looker so your teams always work with fresh, reliable insights.
Google BigQuery + Looker integration
Google BigQuery and Looker are a natural pairing in the modern data stack. BigQuery is a scalable cloud data warehouse; Looker is the BI and analytics layer that turns raw data into dashboards people actually use. Together, they let organizations store massive datasets and surface meaningful insights — but keeping data flows between them timely, accurate, and well-governed takes real orchestration. Integrating BigQuery with Looker through tray.ai eliminates manual handoffs so your analytics layer always reflects the latest state of your data.
When BigQuery and Looker operate in silos, data teams burn enormous time manually triggering refreshes, reconciling stale dashboards, and writing one-off scripts to move or transform data. Business stakeholders lose confidence in reports when numbers go out of date, and engineers end up on maintenance duty instead of building anything new. Integrating BigQuery and Looker on tray.ai lets you automate content delivery scheduling, trigger LookML model refreshes based on upstream pipeline events, export Looker query results back into BigQuery for further analysis, and orchestrate end-to-end BI workflows without custom infrastructure. The result is an analytics environment that keeps pace with your business, cutting time-to-insight and freeing your data team to focus on work that matters.
Automate & integrate Google BigQuery + Looker
Automating Google BigQuery and Looker business processes or integrating data is made easy with Tray.ai.
Use case
Automated Dashboard Refresh After BigQuery Pipeline Completion
When a BigQuery data pipeline or scheduled query finishes loading new data, tray.ai automatically triggers a Looker PDT (persistent derived table) rebuild and dashboard content refresh. Business users never look at stale reports, and there's no manual intervention needed after every ETL run. Teams can trust that dashboards reflect the most current data without tracking pipeline completion themselves.
- Eliminate stale dashboards by refreshing Looker content immediately after BigQuery loads new data
- Remove manual coordination between data engineering and BI teams
- Cut time-to-insight from hours to minutes after pipeline completion
Use case
Export Looker Query Results Back into BigQuery for Advanced Analysis
Automatically export the results of scheduled Looker Looks or dashboard queries directly into BigQuery tables for archival, blending with other datasets, or machine learning model training. This creates a feedback loop where curated business metrics from Looker become inputs for deeper BigQuery-powered analysis. Teams get a historical record of performance snapshots without any manual CSV exports or copy-paste workflows.
- Preserve historical snapshots of Looker metrics inside BigQuery for trend analysis
- Blend Looker-curated KPIs with raw BigQuery datasets for richer modeling
- Eliminate error-prone manual CSV export and upload processes
Use case
Sync New BigQuery Datasets to Looker as Explores Automatically
When a new dataset or table is created in BigQuery — triggered by a data ingestion event or an upstream workflow — tray.ai can notify your data team and automatically update Looker project metadata or kick off LookML generation workflows. Your Looker data model stays in sync with an evolving BigQuery schema without engineers manually updating Explore definitions every time the warehouse changes. Analysts get access to new data sources faster.
- Speed up analyst access to new BigQuery tables by automating Looker model update notifications
- Reduce LookML maintenance burden when BigQuery schemas evolve
- Create an auditable change log linking BigQuery schema events to Looker model updates
Use case
Alert Stakeholders When BigQuery Data Quality Checks Fail Before Looker Reports Run
Before a scheduled Looker report delivery runs, tray.ai can first execute a BigQuery data quality validation query and check the results. If row counts, null rates, or metric thresholds fall outside acceptable ranges, the workflow pauses the Looker delivery and alerts the responsible data engineer via Slack or email. Stakeholders don't receive reports built on corrupt or incomplete data, which protects trust in your analytics environment.
- Prevent distribution of reports built on bad or incomplete BigQuery data
- Automatically notify data engineers of quality issues before they reach business users
- Build a systematic data quality gate between your warehouse and your BI layer
Use case
Schedule and Deliver Looker Reports Triggered by BigQuery Events
Use BigQuery event signals — a table partition completing, a revenue threshold being crossed, a daily batch job finishing — as dynamic triggers for Looker report delivery to specific stakeholders. Rather than relying on fixed-time schedules, this event-driven approach ensures reports go out when the data is actually ready. Executives and team leads get Looker dashboards in their inbox precisely when the underlying BigQuery data is fresh.
- Replace rigid time-based schedules with event-driven report delivery
- Ensure report recipients always receive content backed by fully loaded BigQuery data
- Reduce unnecessary report deliveries when pipeline delays push data availability later than expected
Use case
Centralize Looker Usage Analytics Back into BigQuery for Governance
Pull Looker system activity and usage data — user query logs, dashboard view counts, content engagement metrics — via the Looker API and load it into BigQuery for centralized governance and audit reporting. Data teams get a complete picture of how their analytics content is being used alongside the operational data it describes. BI leaders can identify underused dashboards, power users, and adoption trends across the organization.
- Gain full visibility into Looker content usage patterns stored alongside operational data in BigQuery
- Support data governance and compliance requirements with centralized audit logs
- Identify underperforming dashboards and optimize your Looker content portfolio
Challenges Tray.ai solves
Common obstacles when integrating Google BigQuery and Looker — and how Tray.ai handles them.
Challenge
Managing Stale Looker Dashboards When BigQuery Pipelines Are Delayed
BigQuery data pipelines frequently run on variable schedules due to upstream dependencies, resource contention, or data volume fluctuations. When Looker dashboards refresh on fixed schedules rather than pipeline completion events, stakeholders end up viewing reports built on incomplete or yesterday's data — often with no indication anything is wrong.
How Tray.ai helps
tray.ai workflows monitor BigQuery job completion status in real time and trigger Looker refreshes only after a successful pipeline run is confirmed. Built-in conditional logic and retry handling ensure that if a pipeline is delayed, Looker refreshes wait rather than firing prematurely, and stakeholders get a heads-up about the delay.
Challenge
Handling Large Looker Result Sets When Exporting to BigQuery
Looker API result exports have row limits and timeout constraints, making it difficult to reliably export large datasets back into BigQuery with naive API calls. Custom pagination, chunking logic, and error recovery are typically required — work most teams have to build and maintain manually outside their standard tooling.
How Tray.ai helps
tray.ai has native pagination support and looping constructs that automatically handle large Looker API result sets across multiple pages. Combined with built-in error handling and retry logic, tray.ai workflows reliably export even large Looker datasets into BigQuery without custom engineering or fragile one-off scripts.
Challenge
Keeping Looker Permission Structures in Sync with BigQuery Access Controls
As BigQuery datasets are created, shared, or retired, the corresponding Looker permission structures — user groups, model sets, and data access controls — frequently fall out of sync. Users may end up with access to Looker Explores backed by BigQuery data they shouldn't see, or valid users get blocked from content they should have. Neither outcome is good.
How Tray.ai helps
tray.ai orchestrates end-to-end access provisioning workflows that respond to BigQuery project or dataset changes and automatically update corresponding Looker user groups and permission assignments. Access governance stays consistent across both systems, and every permission change is logged for compliance purposes.
Templates
Pre-built workflows for Google BigQuery and Looker you can deploy in minutes.
BigQuery Pipeline Completion → Looker PDT Rebuild and Dashboard Refresh
Google BigQuery 
Looker Listens for a BigQuery scheduled query or pipeline job completion event, validates that the target tables were updated successfully, then triggers a Looker PDT rebuild and refreshes specified dashboard content so stakeholders always see up-to-date data.
Scheduled Looker Look Export → BigQuery Table Load
Looker Google BigQuery On a defined schedule, runs a Looker Look or dashboard tile query via the Looker API, retrieves the result set, and appends or overwrites a target BigQuery table with the exported data for archival, blending, or downstream ML use.
BigQuery Data Quality Gate Before Looker Report Delivery
Google BigQuery Looker Before a Looker scheduled report is sent to stakeholders, this template runs a suite of data quality checks in BigQuery and conditionally allows or blocks the Looker delivery, alerting the data team if issues are detected.
Looker Usage Analytics → BigQuery Governance Dashboard
Looker Google BigQuery Periodically pulls Looker system activity logs, user query history, and content engagement data via the Looker API and loads the records into a dedicated BigQuery dataset to power a centralized analytics governance and adoption reporting layer.
BigQuery New Table Detection → Looker Model Update Notification
Google BigQuery Looker Monitors a BigQuery dataset for newly created tables or views and automatically notifies the data modeling team in Slack with metadata about the new object, prompting them to update the relevant LookML Explores in Looker.
Event-Driven Looker Report Delivery Triggered by BigQuery Metric Threshold
Google BigQuery Looker Monitors a metric in BigQuery — such as daily revenue, active users, or error rates — and triggers an immediate Looker report delivery to specified stakeholders when the metric crosses a defined threshold, enabling real-time alerting backed by warehouse data.
How Tray.ai makes this work
Google BigQuery + Looker runs on the full Tray.ai platform
Intelligent iPaaS
Integrate and automate across 700+ connectors with visual workflows, error handling, and observability.
Learn more →Agent Builder
Build AI agents that read, write, and take action in Google BigQuery and Looker — with guardrails, audit, and human-in-the-loop.
Learn more →Agent Gateway for MCP
Expose Google BigQuery + Looker actions as governed MCP tools — observable, rate-limited, authenticated.
Learn more →Ship your Google BigQuery + Looker integration.
We'll walk through the exact integration you're imagining in a tailored demo.