# Connect Google Ad Manager to Google BigQuery > Automate ad performance data into BigQuery and get scalable reporting without manual exports. **Canonical page:** https://tray.ai/connectors/google-ad-manager-google-bigquery-integrations/ **Google Ad Manager connector:** https://tray.ai/connectors/google-ad-manager-integrations/ **Google Ad Manager documentation:** https://tray.ai/documentation/connectors/service/google-ad-manager **Google BigQuery connector:** https://tray.ai/connectors/google-bigquery-integrations/ **Google BigQuery documentation:** https://tray.ai/documentation/connectors/service/google-bigquery ## Overview 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. The business case is straightforward: ad revenue decisions are only as good as the data behind them. Manually downloading Ad Manager reports and uploading them to BigQuery is slow, error-prone, and creates data latency that delays optimization decisions. An automated integration keeps impression counts, fill rates, eCPM figures, advertiser spend, and line item performance flowing continuously into BigQuery, where they can be joined with first-party audience data, CRM records, or web analytics to build a complete picture of monetization health. Revenue operations, data engineering, and finance teams get reliable, repeatable reporting pipelines that scale with the ad business. ## Use cases ### 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. - Zero manual intervention in daily data ingestion - Consistent, timestamped records for reliable trend analysis - Finance and ops teams access accurate revenue figures every morning ### 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. - A single view of revenue across all demand channels - Supports custom attribution models beyond Ad Manager's native reports - Reduces analyst time spent manually stitching together CSVs ### 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. - Catch underperforming ad units before revenue is lost - Power ML models with clean, structured historical data - Enables automated floor price recommendations ### 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. - Proactively manage delivery risk for high-value direct campaigns - Historical pacing data informs future order planning - Reduces manual monitoring burden on ad operations teams ### 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. - Quantify the yield lift from audience targeting - Supports data-driven audience packaging for direct sales - Enables granular segment-level reporting not available natively ### 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. - Eliminate manual reconciliation spreadsheets - Accelerate month-end close with automated data pipelines - Auditable revenue records with full lineage in BigQuery ### 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. - Monitor deal health without logging into Ad Manager daily - Compare PMP performance across buyers and time periods in SQL - Data feeds into executive dashboards and QBR reporting ## Templates ### 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. Connectors used: Google Ad Manager, Google BigQuery ### 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. Connectors used: Google Ad Manager, Google BigQuery ### 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. Connectors used: Google Ad Manager, Google BigQuery ### 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. Connectors used: Google Ad Manager, Google BigQuery ### 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. Connectors used: Google Ad Manager, Google BigQuery ### 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. Connectors used: Google Ad Manager, Google BigQuery ## Challenges Tray.ai solves ### 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 helps:** 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. ### 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 helps:** 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. ### 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 helps:** 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. ### 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 helps:** 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. ### 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 helps:** 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. ## Learn more - Intelligent Integration: https://tray.ai/platform/intelligent-ipaas/ - Merlin Agent Builder: https://tray.ai/platform/merlin-agent-builder/ - Agent Gateway for MCP: https://tray.ai/platform/agent-gateway/ - Book a demo: https://tray.ai/contact/