Home > Destinations > BigQuery > Google Merchant Center

Connect Google Merchant Center to BigQuery

Connect Google Merchant Center to Big Query and export selected accounts, metrics, and dimensions to your Google BigQuery project.

Configure the historical window, destination table, and recurring schedule without maintaining a Google Merchant Center API pipeline.

Connect Google Merchant Center to BigQuery

Used by the world's leading companies

Export Google Merchant Center data to Big Query

Catchr handles extraction, normalization, and loading automatically.

Centralize

Centralize Google Merchant Center data in BigQuery

Bring all your Google Merchant Center Metrics data into your warehouse and make it available for analytics, machine learning, and internal applications.

Data Freshness

Keep your warehouse always up to date

Automatically sync new data from Google Merchant Center so every dashboard, model, and report works with fresh information.

Data Freshness

Unify Google Merchant Center with the rest of your business data

Join marketing, finance, CRM, and product data inside the same warehouse.

Google Merchant Center metrics and dimensions available in Google Big Query

Access 28 metrics and 90 dimensions from Google Merchant Center connectors including Clicks, Impressions, CTR, Conversions, Conversion Value, Conversion Rate, Date, Product Title, Product ID, Product Brand Name, Product Category L1, Product Channel, Product Destination Status, Product Availability and many more...

28

Metrics availables

90

Dimensions availables

Connect Google Merchant Center to BigQuery in minutes

Move your data from Google Merchant Center to BigQuery with a simple setup. Once connected, Catchr handles the ingestion automatically so your warehouse stays up to date without manual work.

Step one

Connect your Google Merchant Center account

Authenticate your Google Merchant Center account securely in Catchr. No custom scripts, API maintenance, or engineering work required.

Connect now
Client A · Connected sources
5 sources ready
Google Merchant Center 3 advertising accounts Connected
Google Ads 2 advertising accounts Connected
Google Analytics 4 1 web property Connected
HubSpot 1 CRM portal Connected
Step Two

Choose the Big Query Destination

Select your Google Cloud project and BigQuery dataset. Catchr automatically prepares the destination and maps your data into analytics-ready tables.

Destination / Google BigQuery Required fields
Step three

Keep your warehouse in sync

Catchr continuously imports new and updated data into BigQuery.

Your warehouse stays fresh automatically, ready for SQL, dbt, BI tools, and AI workloads.

Destination / Google BigQuery Example
Project ID Required
agency-reporting-prod
Dataset ID Required
marketing_raw
Service-account JSON key Required
catchr-bigquery-writer.json
Destination table Per job
facebook_ads_daily
Date partition Optional
date
Recurring schedule Choose a cadence
Every 6 hours
Daily · 06:00
Weekly · Mon
First sync scheduled Selected fields · configured history
06:00 UTC

Review 30-day Google Merchant Center product visibility in SQL.

Once the table is available, query the fields you selected and named. This example summarizes product impressions, clicks, conversions, and click-through rate.

Google Merchant Center.sql
BigQuery SQL
30-day window 
SELECT
  product_id,
  SUM(impressions) AS impressions,
  SUM(clicks) AS clicks,
  SUM(conversions) AS conversions,
  SAFE_DIVIDE(
    SUM(clicks),
    SUM(impressions)
  ) AS ctr
FROM `your_project.marketing_raw.google_merchant_center_products_daily`
WHERE date >= DATE_SUB(
  CURRENT_DATE(),
  INTERVAL 30 DAY
)
GROUP BY product_id
ORDER BY clicks DESC;
Illustrative SQL — adapt it to the fields and schema selected in your export. Standard SQL

Connect more table to your Google Merchant Center data

Open the dedicated route for each platform. Its field catalog, reporting grain, historical constraints, and warehouse use cases should be specific to that source.

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Before you schedule the job.

Answers about available Meta Ads data, reporting cadence, history, table settings, attribution changes, and downstream joins.

How do I connect Google Merchant Center to BigQuery?

Add Google Merchant Center as a source in Catchr, choose BigQuery as the destination, select the fields to export, and authorize the target Google Cloud project and dataset.

What Google Merchant Center data can I send to BigQuery?

Export accounts, products, product groups, feeds, statuses, issues, price insights, competitiveness, clicks, impressions, and other available merchant fields.

How often can Google Merchant Center data be refreshed in BigQuery?

Schedule recurring refreshes in Catchr so your BigQuery tables stay updated automatically. Available frequencies depend on your plan, source limits, and reporting needs.

Can I combine Google Merchant Center data with other sources in BigQuery?

Yes. Keep sources in separate tables, then use SQL views or models to join them on shared dimensions such as date, campaign, account, customer, product, or region.

What can I build with Google Merchant Center data in BigQuery?

Build feed-health monitoring, product visibility reports, price comparisons, issue prioritization, and joined advertising and catalog datasets.

Can the table be partitioned by date?

If your selected schema contains an eligible date field, you can configure that field as the optional date partition for the destination table. Catchr does not configure table clustering or design your downstream analytical models.

Can Catchr backfill history into BigQuery?


Yes. Configure the historical period supported by the source for the initial import, then keep the table updated with recurring exports. The available range is determined by the source API and the settings of your job, not an unlimited-history promise.

Still have a question ? 

Our teams is always here to responds to any question you could have about our data connector. 

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