Google Ad Manager + Google BigQuery
Connect Google Ad Manager to Google BigQuery for Smarter Ad Analytics
Automate ad performance data into BigQuery and get scalable reporting without manual exports.
Why integrate Google Ad Manager and Google BigQuery?
Google Ad Manager and Google BigQuery are a natural pairing for any publisher or advertiser who's hit the ceiling on built-in reporting. Ad Manager generates a lot of impression, click, revenue, and yield data, but its native reporting tools get limiting fast when you need cross-channel analysis, long-term trend tracking, or custom attribution models. Integrating Ad Manager with BigQuery lets teams automatically stream ad performance data into a scalable data warehouse, where it's ready for SQL-based analysis, BI dashboards, and data science workflows.
Automate & integrate Google Ad Manager & Google BigQuery
Use case
Automated Daily Ad Performance Reporting
Schedule a recurring workflow that pulls yesterday's Ad Manager delivery data — including impressions, clicks, revenue, and viewability — and loads it into a dedicated BigQuery dataset. This eliminates manual report downloads and keeps your data warehouse current.
Use case
Cross-Channel Revenue Attribution
Combine Ad Manager line item and advertiser revenue data in BigQuery alongside data from other ad platforms for unified cross-channel attribution modeling. Analysts can write SQL queries that span programmatic, direct-sold, and third-party demand sources in a single table.
Use case
Yield Optimization and eCPM Trend Analysis
Stream Ad Manager network performance metrics into BigQuery on a rolling basis and use them to identify eCPM trends by ad unit, placement, device, or geography. Data science teams can build predictive models that inform pricing floors and inventory packaging decisions.
Use case
Advertiser and Order-Level Pacing Monitoring
Pull Ad Manager delivery data at the order and line item level into BigQuery at regular intervals to track pacing against contracted goals. Trigger alerts or downstream workflows when campaigns are over-delivering or at risk of under-delivery.
Use case
Audience Segment Performance Analysis
Load Ad Manager audience segment and key-value targeting data into BigQuery and join it with first-party audience profiles or CDP data. Publishers can then quantify the revenue premium of targeted versus non-targeted inventory at scale.
Use case
Billing Reconciliation and Finance Reporting
Automate the extraction of Ad Manager recognized revenue and invoicing data into BigQuery, where it can be reconciled against CRM deal values, order management systems, or ERP records. Finance teams get a single source of truth for monthly close processes.
Use case
Programmatic Deal and PMP Performance Tracking
Continuously sync Private Marketplace and programmatic guaranteed deal metrics from Ad Manager into BigQuery to give yield and partnerships teams real-time visibility into deal health, bid density, and clearing prices across buyers.
Get started with Google Ad Manager & Google BigQuery integration today
Google Ad Manager & Google BigQuery Challenges
What challenges are there when working with Google Ad Manager & Google BigQuery and how will using Tray.ai help?
Challenge
Ad Manager API Report Generation Latency
Ad Manager reports aren't returned synchronously. The API requires you to submit a report job, poll for completion, and then download the result. This async pattern is hard to manage reliably in custom scripts and can fail silently if the polling logic is sloppy.
How Tray.ai Can Help:
Tray.ai's workflow engine natively handles asynchronous API patterns with built-in polling loops and conditional branching. You configure the workflow to submit the report job, wait and poll at intervals until the report is ready, then download and load the data — no custom retry or state management code required.
Challenge
BigQuery Schema Management and Data Type Mismatches
Ad Manager reports return data in CSV or XML format with string-typed fields that need to be cast to the right BigQuery types — integers, floats, timestamps, dates — before loading. When those types don't line up with your BigQuery schema, you get load failures that are annoying to debug.
How Tray.ai Can Help:
Tray.ai's data transformation operators let you map, cast, and reshape Ad Manager report fields before they reach BigQuery. You define type conversions and field mappings in a visual interface, and the BigQuery connector can enforce schema-on-write, so bad data gets caught before it causes a failed ingestion job.
Challenge
Handling API Rate Limits During High-Volume Backfills
When backfilling or syncing large volumes of Ad Manager data, workflows can burn through the Ad Manager API's rate limits quickly. That means throttled requests, incomplete data loads, and no clean way to resume without building custom checkpointing logic yourself.
How Tray.ai Can Help:
Tray.ai has built-in rate limit handling with configurable retry logic, exponential backoff, and delay operators. For large backfill workflows, you can add explicit throttle steps between API calls inside loops. And because tray.ai's workflow execution is durable, a throttled or failed step retries from exactly where it left off — it won't reprocess data that already loaded.
Challenge
Keeping BigQuery Tables Deduplicated on Upserts
Repeatedly loading Ad Manager data into BigQuery can produce duplicate rows when workflows re-process overlapping date ranges or when a report gets fetched more than once after a failure and retry. BigQuery's append-only nature means deduplication isn't trivial without a real merge strategy.
How Tray.ai Can Help:
Tray.ai's BigQuery connector supports MERGE operations and insert-or-update patterns. You define a unique key — like line item ID plus date — and the connector upserts records rather than blindly appending them. Your BigQuery tables stay clean and accurate even when workflows are retried or cover overlapping date ranges.
Challenge
Credential and Permission Management Across Google Products
Connecting two Google products sounds simple, but OAuth scope mismatches are common. The service account or OAuth credentials you use need the right permissions for both the Ad Manager API and BigQuery dataset writes. Get that wrong and you'll see silent authorization failures that are genuinely hard to track down.
How Tray.ai Can Help:
Tray.ai gives you a centralized place to configure OAuth credentials and service account keys for Google Ad Manager and Google BigQuery independently. Connector authentication gets validated at setup time, and permission errors surface with clear, actionable messages — so you catch credential problems before they take down a production workflow.
Start using our pre-built Google Ad Manager & Google BigQuery templates today
Start from scratch or use one of our pre-built Google Ad Manager & Google BigQuery templates to quickly solve your most common use cases.
Google Ad Manager & Google BigQuery Templates
Find pre-built Google Ad Manager & Google BigQuery solutions for common use cases
Template
Daily Ad Manager Report to BigQuery Pipeline
Runs on a daily schedule to query Ad Manager for the previous day's network performance report — covering impressions, clicks, CTR, revenue, and fill rate — and appends the results as a new partition in a BigQuery table.
Steps:
- Trigger workflow on a daily schedule at a configured time
- Call Ad Manager Reporting API to generate and download the previous day's network performance report
- Parse and transform report data into BigQuery-compatible row format
- Insert rows into the target BigQuery table with a date partition
- Log success or send a Slack alert on failure
Connectors Used: Google Ad Manager, Google BigQuery
Template
Ad Manager Line Item Delivery Sync to BigQuery
Pulls line item and order delivery data from Ad Manager at a configurable interval and upserts records into BigQuery, enabling pacing dashboards and delivery reconciliation against contracted goals.
Steps:
- Trigger on a scheduled interval (e.g., every 6 hours)
- Query Ad Manager for active orders and their associated line item delivery stats
- Transform line item data including impressions, budget consumed, and pacing percentage
- Upsert records into BigQuery using line item ID as the merge key
- Optionally trigger a downstream alert if any line item pacing falls below threshold
Connectors Used: Google Ad Manager, Google BigQuery
Template
Ad Manager Audience Segment Data to BigQuery
Extracts audience segment targeting data and associated performance metrics from Ad Manager and loads them into BigQuery for join analysis with first-party audience data stored in the same warehouse.
Steps:
- Schedule a weekly workflow to pull audience segment definitions and IDs from Ad Manager
- Retrieve performance metrics per segment from the Ad Manager Reporting API
- Flatten and normalize segment data into a relational schema
- Load records into a BigQuery audience performance table
- Trigger a Looker Studio or Data Studio refresh notification upon completion
Connectors Used: Google Ad Manager, Google BigQuery
Template
Ad Manager PMP Deal Metrics to BigQuery
Syncs Private Marketplace deal-level metrics from Ad Manager into BigQuery on a recurring basis, powering deal health dashboards and buyer performance analysis for programmatic sales teams.
Steps:
- Trigger workflow on a daily or hourly schedule
- Query Ad Manager for all active PMP and programmatic guaranteed deal IDs
- Fetch bid density, clearing price, impressions won, and revenue per deal
- Append deal performance rows into BigQuery with a timestamp
- Update a connected Looker or BI dashboard dataset
Connectors Used: Google Ad Manager, Google BigQuery
Template
Ad Manager Historical Backfill to BigQuery
A one-time or on-demand workflow that backfills up to 90 days of Ad Manager historical performance data into BigQuery, giving you a baseline dataset for trend analysis and machine learning model training.
Steps:
- Accept a configurable start and end date as workflow inputs
- Loop through each date in the range and request daily Ad Manager reports
- Handle API rate limits with built-in retry and throttling logic
- Insert each day's data as a separate partition in BigQuery
- Send a completion summary with row counts loaded per date
Connectors Used: Google Ad Manager, Google BigQuery
Template
Ad Manager Revenue to BigQuery for Finance Reconciliation
Extracts recognized revenue and CPM data from Ad Manager monthly reports and loads structured records into BigQuery where finance teams can reconcile against CRM and ERP data for billing and close processes.
Steps:
- Trigger at month-end on a scheduled date
- Request an Ad Manager revenue report scoped to the prior month by advertiser and order
- Parse revenue figures and map to internal order IDs
- Load reconciled records into a BigQuery finance table
- Notify the finance team via email with a row count and revenue total summary
Connectors Used: Google Ad Manager, Google BigQuery