Google BigQuery + Looker
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.
Why integrate Google BigQuery and Looker?
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.
Automate & integrate Google BigQuery & Looker
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.
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.
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.
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.
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.
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.
Use case
Automate Looker Embed and API Token Provisioning When BigQuery Projects Are Created
When a new BigQuery project or dataset is provisioned for a business unit, tray.ai can automatically trigger Looker user group creation, permission assignments, and embed token provisioning to match the new data access scope. New teams get access to the right Looker content connected to their BigQuery data without requiring manual setup from a data platform administrator. Access governance stays consistent and auditable across the full stack.
Get started with Google BigQuery & Looker integration today
Google BigQuery & Looker Challenges
What challenges are there when working with Google BigQuery & Looker and how will using Tray.ai help?
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 Can Help:
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 Can Help:
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 Can Help:
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.
Challenge
Orchestrating Multi-Step Workflows Across BigQuery and Looker Without Custom Code
Connecting BigQuery and Looker in sophisticated ways — running quality checks before report delivery, exporting results after refreshes, chaining multiple API calls conditionally — typically requires custom Python or Airflow DAGs that data teams must build, test, deploy, and maintain. That's a real ongoing engineering cost, especially as business requirements change.
How Tray.ai Can Help:
tray.ai's visual workflow builder lets you construct complex multi-step automations across BigQuery and Looker without writing custom orchestration infrastructure. Native connectors for both services expose the full API surface of each platform, and tray.ai's branching, looping, error handling, and scheduling capabilities handle the orchestration complexity that would otherwise require bespoke engineering.
Challenge
Avoiding Duplicate Data Loads When Retrying Failed BigQuery-to-Looker Workflows
When a workflow that loads data into BigQuery or triggers a Looker refresh fails midway and is retried, you risk duplicate records being inserted into BigQuery tables or redundant PDT rebuilds consuming unnecessary warehouse compute. Without idempotency controls, retries can corrupt data or inflate costs.
How Tray.ai Can Help:
tray.ai workflows support idempotency patterns through built-in state management and conditional logic that checks whether a given operation has already completed successfully before re-executing it. Combined with BigQuery's support for merge operations and Looker's job status checks, retried workflows are safe and don't produce duplicate side effects.
Start using our pre-built Google BigQuery & Looker templates today
Start from scratch or use one of our pre-built Google BigQuery & Looker templates to quickly solve your most common use cases.
Google BigQuery & Looker Templates
Find pre-built Google BigQuery & Looker solutions for common use cases
Template
BigQuery Pipeline Completion → Looker PDT Rebuild and Dashboard Refresh
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.
Steps:
- Detect BigQuery scheduled query or batch job completion via polling or Pub/Sub event
- Run a validation query in BigQuery to confirm row counts and data freshness thresholds are met
- Trigger Looker PDT rebuild via the Looker API for affected Explores
- Refresh specified Looker dashboards and send a Slack notification confirming update completion
Connectors Used: Google BigQuery, Looker
Template
Scheduled Looker Look Export → BigQuery Table Load
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.
Steps:
- Trigger on a tray.ai schedule (hourly, daily, or custom)
- Execute specified Looker Look or dashboard query via the Looker Run Look API endpoint
- Transform and format the JSON result set into BigQuery-compatible row format
- Load transformed data into the target BigQuery table using streaming inserts or batch load
Connectors Used: Looker, Google BigQuery
Template
BigQuery Data Quality Gate Before Looker Report Delivery
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.
Steps:
- Trigger on a schedule aligned with Looker report delivery time
- Run data quality validation queries in BigQuery checking row counts, null rates, and metric bounds
- Evaluate results against defined thresholds using tray.ai conditional logic
- If checks pass, trigger Looker scheduled report delivery; if checks fail, suppress delivery and alert the data engineering team via email or Slack
Connectors Used: Google BigQuery, Looker
Template
Looker Usage Analytics → BigQuery Governance Dashboard
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.
Steps:
- Trigger on a daily schedule
- Query Looker System Activity Explores or API endpoints for user events, query history, and dashboard views
- Paginate through result sets and normalize data into a consistent schema
- Insert records into partitioned BigQuery tables for governance reporting and trend analysis
Connectors Used: Looker, Google BigQuery
Template
BigQuery New Table Detection → Looker Model Update Notification
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.
Steps:
- Poll BigQuery INFORMATION_SCHEMA on a scheduled interval to detect new tables or views
- Compare detected objects against a previously stored state to identify net-new additions
- Retrieve table metadata including schema, row count, and owning dataset
- Post a structured Slack notification to the data modeling team with table details and a link to the relevant Looker project for LookML updates
Connectors Used: Google BigQuery, Looker
Template
Event-Driven Looker Report Delivery Triggered by BigQuery Metric Threshold
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.
Steps:
- Run a parameterized BigQuery query on a frequent polling schedule to retrieve the current metric value
- Compare the result against a configured threshold using tray.ai conditional branching
- If threshold is breached, trigger the relevant Looker Look or dashboard delivery via the Looker Scheduler API
- Log the alert event back to a BigQuery audit table with timestamp and metric value for historical tracking
Connectors Used: Google BigQuery, Looker