Looker connector
Automate Business Intelligence Workflows with Looker Integrations
Connect Looker to your tech stack to sync data, trigger actions from insights, and embed analytics into automated workflows.
What can you do with the Looker connector?
Looker is a business intelligence and data exploration platform built for data-driven organizations. Integrating Looker with your CRM, marketing tools, data warehouses, and communication platforms lets teams act on insights automatically rather than waiting for someone to check a dashboard. With tray.ai, you can build workflows that pull Looker data into operational systems, trigger alerts from dashboard anomalies, and keep your analytics environment in sync with the rest of your business.
Automate & integrate Looker
Automating Looker business process or integrating Looker data is made easy with tray.ai
Use case
Automated Insight-to-Action Workflows
When a Looker Look or dashboard query returns results that hit a defined threshold — a spike in churn risk or a drop in conversion rate — automatically trigger downstream actions in Salesforce, Slack, or your CRM. This closes the gap between spotting a problem and doing something about it, without anyone having to manually check a dashboard first.
Use case
Scheduled Report Distribution and Data Delivery
Stop manually exporting Looker results and emailing them around. Automate extraction, transformation, and delivery of query results to stakeholders via email, Slack, Google Sheets, or cloud storage. Set any schedule you need and route data to the right destination based on content, team, or region.
Use case
CRM and Sales Data Enrichment from Looker
Pull aggregated metrics and behavioral signals from Looker — product usage scores, revenue trends, pipeline velocity — and write them back into Salesforce or HubSpot as custom fields. Sales and customer success teams get the analytical context they need directly in the tools they're already working in.
Use case
Data Pipeline Monitoring and Alerting
Use Looker queries to monitor the health of your data pipelines and warehouse tables — checking for row count anomalies, null rates, or freshness gaps — and automatically alert data engineering teams via PagerDuty, Slack, or Jira when something looks off.
Use case
Customer Health Scoring and Lifecycle Automation
Run Looker queries that calculate customer health scores based on product usage, support tickets, and engagement metrics, then sync those scores to your customer success platform or CRM. From there, trigger lifecycle automations like QBR scheduling, at-risk alerts, or renewal workflows automatically.
Use case
Marketing Performance Sync and Campaign Optimization
Extract campaign performance metrics from Looker and push them into marketing platforms, spreadsheets, or executive dashboards. You can also trigger budget adjustment workflows or pause underperforming campaigns based on thresholds you define directly in Looker — no analyst required to kick things off.
Use case
Embedded Analytics and AI Agent Data Retrieval
Use Looker as a data retrieval layer for AI agents and chatbots, so conversational interfaces can query business metrics on demand. When a Slack bot or AI assistant needs to answer a question about revenue, churn, or pipeline, it can run a Looker query dynamically and return structured results.
Build Looker Agents
Give agents secure and governed access to Looker through Agent Builder and Agent Gateway for MCP.
Data Source
Run Looker Looks
Execute saved Looks to retrieve pre-built query results and use the data as context for analysis or decision-making. An agent can surface specific business metrics without constructing queries from scratch.
Data Source
Query Explores Directly
Run ad hoc queries against Looker Explores to retrieve tailored datasets based on dynamic conditions. An agent can answer specific business questions by pulling only the relevant dimensions and measures.
Data Source
Fetch Dashboard Data
Pull all tiles and underlying data from a Looker dashboard for a full snapshot of business performance. Agents can use this to write summaries, spot anomalies, or kick off downstream workflows based on what the dashboard shows.
Data Source
Look Up User and Group Information
Retrieve details about Looker users, groups, and their assigned roles or permissions. Useful for agents managing access control workflows or auditing who has visibility into specific data.
Data Source
Retrieve LookML Model Metadata
Access metadata about LookML models, Explores, dimensions, and measures to understand how available data is structured. Agents can use this to point users toward the right data sources or validate query parameters before running them.
Data Source
Monitor Scheduled Deliveries
Fetch details about scheduled data deliveries and reports configured in Looker. An agent can use this to audit delivery status, catch failures, or confirm that critical reports are going out on time.
Agent Tool
Create and Update Looks
Programmatically create new saved Looks or update existing ones with revised queries or display settings. An agent can automate the setup of reporting artifacts in response to business requests.
Agent Tool
Schedule Report Delivery
Configure or trigger scheduled deliveries of Looks and dashboards to destinations like email or cloud storage. An agent can automate recurring report distribution without anyone touching the Looker UI.
Agent Tool
Manage User Access and Permissions
Create, update, or deactivate Looker users and adjust their group memberships or role assignments. Agents can automate user provisioning and deprovisioning as part of broader identity management workflows.
Agent Tool
Render and Export Visualizations
Render Looker content like Looks or dashboards into image or PDF formats for use in reports, presentations, or notifications. An agent can attach these exports to emails, Slack messages, or document repositories automatically.
Agent Tool
Create and Manage Folders
Organize Looker content by creating or updating folders and managing their permissions. Agents can automate content organization as teams or projects are created or restructured.
Agent Tool
Trigger and Monitor Running Queries
Initiate queries programmatically and track their execution status within Looker. This lets agents orchestrate data retrieval across multi-step workflows and handle results when queries complete.
Get started with our Looker connector today
If you would like to get started with the tray.ai Looker connector today then speak to one of our team.
Looker Challenges
What challenges are there when working with Looker and how will using Tray.ai help?
Challenge
Bridging the Gap Between Analytics and Operational Systems
Looker surfaces insights well, but getting those insights into the CRMs, ticketing tools, and messaging platforms where work actually happens requires custom scripting or manual exports. Teams end up copying data by hand or maintaining fragile one-off scripts.
How Tray.ai Can Help:
tray.ai has pre-built Looker connector actions alongside connectors for Salesforce, HubSpot, Jira, and hundreds of other tools, so you can build bi-directional data flows with a visual workflow builder without writing custom code.
Challenge
Managing Complex Looker API Authentication and Query Construction
The Looker API requires OAuth token management, nuanced query construction using the Looker query object model, and careful handling of rate limits and pagination. That's a lot of development overhead before you've written a single line of actual integration logic.
How Tray.ai Can Help:
tray.ai handles Looker OAuth authentication natively and abstracts the query API into simple, configurable actions. You can run inline queries, retrieve Looks, and manage schedules without writing API boilerplate or managing token refresh logic.
Challenge
Keeping Downstream Systems in Sync with Evolving LookML Models
As LookML models evolve — fields get renamed, explores get restructured, new dimensions get added — hardcoded integrations that reference specific field names break silently, producing incorrect or missing data in connected systems.
How Tray.ai Can Help:
When LookML changes, you update the affected workflow in one place rather than hunting down scattered scripts. Field mappings are managed visually, and workflow versioning lets you test against schema changes before pushing anything to production.
Challenge
Scaling Scheduled Delivery Beyond Looker's Native Scheduler
Looker's native scheduled plans have real limitations around conditional logic, multi-destination routing, and data transformation before delivery. Teams that need to send different data subsets to different tools based on dynamic rules tend to outgrow native scheduling quickly.
How Tray.ai Can Help:
tray.ai workflows replace or augment Looker's native scheduler with full conditional branching, data transformation, and multi-connector routing. You can filter, reshape, and route Looker query results to any combination of destinations based on the content of the data itself.
Challenge
Enabling Real-Time Action Without Overloading Looker's API
Teams that want near-real-time automation based on Looker data often end up polling the API too frequently, burning query credits, and hitting rate limits — degrading performance for other users and driving up infrastructure costs.
How Tray.ai Can Help:
tray.ai supports intelligent polling schedules, caching patterns, and webhook-based triggers that cut down unnecessary Looker API calls. Workflows can be designed to run only when data actually changes or when upstream pipeline completions signal that fresh data is available.
Talk to our team to learn how to connect Looker with your stack
Find the tray.ai connector with one of the 700+ other connectors in the tray.ai connector library to integrate your stack.
Integrate Looker With Your Stack
The Tray.ai connector library can help you integrate Looker with the rest of your stack. See what Tray.ai can help you integrate Looker with.
+Google BigQuery & Looker
Learn how to integrate Google BigQuery and Looker together
+Snowflake & Looker
Learn how to integrate Snowflake and Looker together
+AWS Redshift & Looker
Learn how to integrate AWS Redshift and Looker together
+Microsoft SQL Database & Looker
Learn how to integrate Microsoft SQL Database and Looker together
+Google Analytics & Looker
Learn how to integrate Google Analytics and Looker together
+Salesforce & Looker
Learn how to integrate Salesforce and Looker together
+Mixpanel & Looker
Learn how to integrate Mixpanel and Looker together
+Marketo & Looker
Learn how to integrate Marketo and Looker together
Start using our pre-built Looker templates today
Start from scratch or use one of our pre-built Looker templates to quickly solve your most common use cases.
Template
Looker Threshold Alert to Slack and Jira
Runs a scheduled Looker query, evaluates the results against configurable thresholds, posts an alert message in Slack, and automatically creates a Jira ticket for the relevant team when a threshold is breached.
Steps:
- Schedule a Looker query run at a defined interval (hourly, daily, etc.)
- Evaluate returned metric values against user-defined threshold conditions
- Post a formatted alert to a designated Slack channel with the metric details
- Create a Jira issue with severity, description, and assignee based on the alert type
Connectors Used: Looker, Slack, Jira
Template
Looker Report to Google Sheets Daily Sync
Automatically runs a Looker Look or query on a daily schedule, extracts the resulting data, and writes it into a specified Google Sheet tab — overwriting or appending rows based on configuration.
Steps:
- Trigger the workflow on a daily schedule or cron expression
- Run a specified Looker Look or inline query and retrieve results as structured data
- Clear the target Google Sheet tab and write new rows with the latest results
- Notify a Slack channel or email recipient that the sheet has been updated
Connectors Used: Looker, Google Sheets
Template
Looker Customer Health Score Sync to Salesforce
Runs a Looker query to retrieve calculated customer health scores, then upserts those scores as custom fields on the corresponding Salesforce Account records to power CS team workflows.
Steps:
- Run a scheduled Looker query that returns account IDs and computed health scores
- Iterate over each result row and map fields to Salesforce Account field names
- Upsert each Account record in Salesforce with the latest health score and score components
- Log sync results and alert on any failed upsert records
Connectors Used: Looker, Salesforce
Template
New Looker Alert Trigger to HubSpot Workflow Enrollment
Listens for Looker scheduled plan deliveries or threshold alerts and automatically enrolls matching HubSpot contacts or companies into designated workflows for follow-up, nurture, or escalation.
Steps:
- Receive a Looker scheduled plan delivery via webhook or polling
- Parse the delivered data to identify matching contact or company identifiers
- Look up corresponding records in HubSpot by email or company domain
- Enroll matched records into the appropriate HubSpot workflow or sequence
Connectors Used: Looker, HubSpot
Template
Looker Data Export to Amazon S3 for Archiving
Runs a Looker query on a scheduled basis and uploads the resulting CSV or JSON data to a specified Amazon S3 bucket path for long-term storage, compliance archiving, or downstream pipeline ingestion.
Steps:
- Trigger the workflow on a scheduled cadence (daily, weekly, or monthly)
- Execute the Looker query and download results in CSV or JSON format
- Generate a timestamped file name and upload the file to the designated S3 bucket and prefix
- Send a confirmation notification with file path and row count on successful upload
Connectors Used: Looker, Amazon S3
Template
AI Agent Query Router Using Looker Metrics
Lets an AI agent or Slack chatbot accept natural language questions, translate them into Looker API query parameters, run the query, and return a formatted answer with the metric results.
Steps:
- Receive a user question via Slack slash command or tray.ai AI agent trigger
- Pass the question to OpenAI to extract intent and map it to a Looker explore and field set
- Execute the Looker query via API using the extracted parameters
- Format the query results and post the answer back to the user in Slack
Connectors Used: Looker, Slack, OpenAI