Home > Destinations > BigQuery > Facebook Review

Connect Facebook Review to BigQuery

Connect Facebook Review 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 Facebook Review API pipeline.

Connect Facebook Review to BigQuery

Used by the world's leading companies

Export Facebook Review data to Big Query

Catchr handles extraction, normalization, and loading automatically.

Centralize

Centralize Facebook Review data in BigQuery

Bring all your Facebook Review 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 Facebook Review so every dashboard, model, and report works with fresh information.

Data Freshness

Unify Facebook Review with the rest of your business data

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

Facebook Review metrics and dimensions available in Google Big Query

Access 4 metrics and 23 dimensions from Facebook Review connectors including Average Rating, Review Count, Rating, Comments Count, Review Date, Recommendation Status, Reviewer Name, Review Text, Review URL and many more...

4

Metrics availables

23

Dimensions availables

Connect Facebook Review to BigQuery in minutes

Move your data from Facebook Review 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 Facebook Review account

Authenticate your Facebook Review account securely in Catchr. No custom scripts, API maintenance, or engineering work required.

Connect now
Client A · Connected sources
5 sources ready
Facebook Review 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 Facebook Review customer review performance in SQL.

Once the table is available, query the fields you selected and named. This example summarizes review volume and average rating by location over the last 30 days.

Facebook Review.sql
BigQuery SQL
30-day window 
SELECT
  location_name,
  AVG(rating) AS average_rating,
  COUNT(*) AS reviews
FROM `your_project.marketing_raw.facebook_review_reviews`
WHERE review_date >= DATE_SUB(
  CURRENT_DATE(),
  INTERVAL 30 DAY
)
GROUP BY location_name
ORDER BY reviews DESC;
Illustrative SQL — adapt it to the fields and schema selected in your export. Standard SQL

Connect more table to your Facebook Review 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 Facebook Review to BigQuery?

Add Facebook Review 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 Facebook Review data can I send to BigQuery?

Export reviews, reviewers, ratings, replies, dates, locations, languages, topics, and other available reputation fields from this review source.

How often can Facebook Review 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 Facebook Review 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 Facebook Review data in BigQuery?

Build reputation dashboards, rating trends, location comparisons, response monitoring, sentiment workflows, and cross-platform review reporting.

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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