How to Load Data From Facebook Ads to BigQuery (3 Proven Methods for 2026)

While Facebook Ads data is vital for campaign decisions, it’s hard to analyze data or trust it at scale in While you cannot do without Facebook Ads data for campaign decisions, it’s hard to analyze or trust at scale inside Ads Manager, and harder still to combine with data from other tools. 

BigQuery solves that. It gives you one place to store Facebook Ads data alongside your CRM, product, and revenue data, so reporting becomes faster and cheaper once everything lives together.

Getting the data there, though, isn’t always simple. 

Schema changes, API limits, and refresh logic can turn into ongoing maintenance work. So in this guide, we’ll cover how to load data from Facebook Ads to BigQuery, the benefits of doing it and how Hevo keeps the whole transfer reliable at scale.

Let’s get started.

Prerequisites to Integrate Facebook Ads into BigQuery

To transfer data from Facebook Ads to BigQuery, you need to meet the following requires from both platforms:

Facebook Ads:

• Facebook developer app with the Business app type 
• A valid access token with relevant permissions
• App ID (clientID) and clientSecret from the Facebook app dashboard

BigQuery:

• An active Google Cloud project with BigQuery APIs enabled.
• A BigQuery dataset to store Facebook Ads data.
• Appropriate IAM roles for the account or service account doing the load

Learn More: What is ETL? Guide to Extract, Transform, Load Your Data

How to Load Data From Facebook Ads to BigQuery

In this guide, we cover three methods for loading data from Facebook Ads into BigQuery. 

Before we dig into each one, here’s a quick side-by-side:

Method	Best for	Pros	Cons
Hevo (No-code pipeline)	Teams that want accurate, always-fresh ad data	No code, automatic schema handling, API change management, incremental syncs, built-in retries, scheduling handled for you	Limited deep customization
Custom code (API / Webhooks)	Engineering-reliant teams with custom needs	Full control, custom transformations, and real-time ingestion possible	High setup time, constant maintenance, breaks when API versions retire, scaling issues
Manual export and upload	One-time or ad-hoc reporting	Simple, no tools required	Manual effort, data delays, high error risk, no automation, not scalable

Method #1: Automate Facebook Ads Data Transfer to BigQuery Using Hevo

Hevo is a no-code data pipeline that moves data straight into warehouses like BigQuery. It keeps up with Facebook Ads API changes, schema updates, and refreshes on its own, so you never have to look after scripts or jobs. If your team wants steady, hands-off data transfer without writing code, this is the simplest route, and it pairs well with other Facebook Ads automation workflows.

Here’s how to integrate Facebook Ads with BigQuery using Hevo.

Step #1: Set up Facebook Ads as your source

To configure Facebook Ads as the source:

• In the navigation bar, click PIPELINES, then + Create Pipeline.
• Pick Facebook Ads as the source, then choose your destination type.

• Either select an account you’ve already connected, or click Add Facebook Ads Account.
• Log in to Facebook, click Continue as [Company Name].

• Then Save to let Hevo access your data. 

• Confirm with Got it.

Next, on the source settings page, fill in your pipeline name, the ad account you want to pull from, your report type, the attribution window, and how far back you want to sync historical data. Then click Continue.

Proceed to configure the data ingestion and set up the Destination.

Step #2: Set up BigQuery as your destination

To configure BigQuery as the destination:

• Click DESTINATIONS in the navigation bar, then + Create Standard Destination.
• Choose Google BigQuery as the type.
• Enter a destination name, your authentication account, and your project ID.
• For the dataset and GCS bucket, either let Hevo create them for you or pick existing ones.
• Click Test Connection, then Save & Continue.

Step #3: Turn on the pipeline

Once the source and destination are set, the only thing left is to activate the pipeline. 

Open it from the navigation, click Activate Pipeline, and your Facebook Ads data will start flowing into BigQuery on schedule.

Learn More: 20 Best ETL Tools You Should Know About in 2026

Pros and Cons of Using Hevo to Transfer Data Automatically

Automating the transfer between Facebook Ads and BigQuery with Hevo comes with several benefits:

• No-code setup: Hevo connects to 150+ sources natively, Facebook Ads included, without scripts or OAuth flows. That cuts setup time and removes the engineering dependency.
• Built-in refresh logic: Hevo handles incremental loads, historical backfills, and retries, so your data stays current and consistent.
• Scales with data volume: As ad spend, campaigns, or accounts grow, the pipeline keeps running without manual tuning.
• Automatic schema handling: When Facebook adds, removes, or changes fields, Hevo adapts the schema for you. You won’t need to rewrite queries or redeploy pipelines each time the API shifts.
• Low maintenance: Hevo manages error handling, API limits, and token refreshes, which keeps day-to-day overhead low.

That said, Hevo has a few trade-offs for teams that want deeper control:

• Limited customization: You work within predefined schemas and platform logic.
• Less control over transformations: For unusual internal business logic, you may need workarounds rather than full freedom.

Learn More: Azure Data Factory ETL Tutorial: Step-by-Step Guide

Method #2: Transfer Facebook Ads Data to BigQuery Using Custom Code

If you’re comfortable writing code, you can build the transfer yourself. There are two stages here: first you pull the data out of Facebook, then you load it into BigQuery.

Step #1: Get your Facebook Ads data

You can do this in one of two ways.

Option A: Pull data with the Facebook Marketing API

First, set up access:

1. Go to developers.facebook.com and log in with your Facebook credentials.
2. Click My Apps → Create App, then choose the Business app type. Give it a name, add your contact email, and finish the security check.
3. In your app dashboard, find Add a Product and set up the Marketing API.
4. Open the Access Token Tool, generate a User Access Token, and grant the permissions you’ll need: ads_read, ads_management, and business_management. Copy the token, since every API call uses it.

Once that’s done, pull your ad performance data with a Graph API request:

curl -G \ -d ‘access_token=<ACCESS_TOKEN>’ \ -d ‘fields=campaign_name,spend,impressions,clicks’ \ “https://graph.facebook.com/v25.0/act_<AD_ACCOUNT_ID>/insights”

Option B: Get real-time updates with Webhooks

If you’d rather have Facebook push updates the moment a campaign, ad, or metric changes, use Webhooks instead of pulling data yourself.

1. Create a Facebook App

If you haven’t already, follow the same steps as Option A.

2. Build a webhook endpoint on your server

It needs to be an HTTPS endpoint that can handle two things: a GET request for verification (Facebook sends a challenge code) and a POST request carrying the actual updates.

# Pseudocode example def webhook_verify(request):     if request.GET[‘hub.verify_token’] == ‘your_custom_token’:         return request.GET[‘hub.challenge’]     else:         return 403

3. Subscribe to the webhooks

In your app dashboard, go to Webhooks → Add Callback URL, enter your HTTPS URL and a verify token, then subscribe to the fields you care about, such as ads, campaigns, adsets, and insights.

4. Grant permissions and subscribe to an ad account

Using a token with the right permissions (ads_management, pages_manage_ads, and so on), subscribe to the webhook to your ad account:

curl -X POST \ “https://graph.facebook.com/v25.0/<APP_ID>/subscriptions” \ -F “object=ad_account” \ -F “callback_url=https://yourserver.com/webhook” \ -F “fields=ads,insights” \ -F “verify_token=your_custom_token” \ -F “access_token=YOUR_APP_ACCESS_TOKEN”

After that, Facebook sends an update to your endpoint every time a tracked event happens. That’s handy for live performance monitoring, triggering alerts, or feeding a warehouse like BigQuery through an ETL pipeline.

Step #2: Load the data into BigQuery

Once you’ve got the data, there are two ways to get it into BigQuery.

Option A: Go through Google Cloud Storage (GCS)

This is the usual choice for batch files like CSV or JSON.

1. Save your ad data as a file

Use .csv for simple tables or .json for nested data like ad sets and campaign details. Make sure the format lines up with BigQuery’s supported data types.

2. Upload the file to a GCS bucket

You can drag it in through the Google Cloud Console, or use the command line:

gsutil cp ./facebook_data.json gs://your-bucket-name/

3. Create a load job in BigQuery

Open BigQuery in the Cloud Console, click Create Table, set the source to Google Cloud Storage, and pick your file. Choose the format (CSV or JSON), then either define the schema yourself or let BigQuery auto-detect it. Finish by clicking Create Table.

Option B: Load straight through the BigQuery API

If you’d rather skip GCS, you can send data right into BigQuery over HTTP.

1. Format your data as JSON that BigQuery accepts:

[   {“campaign_name”: “Summer Sale”, “impressions”: 12000, “clicks”: 950, “spend”: 340.75, “date”: “2025-07-15”},   {“campaign_name”: “Back to School”, “impressions”: 18000, “clicks”: 1200, “spend”: 475.00, “date”: “2025-07-16”} ]

2. Set up authentication.

You need authentication with either a service account key file (for server-to-server apps) or an OAuth token (for user-based apps). The credentials need bigquery.tables.insertData and bigquery.jobs.create permissions.

3. Send the data

Send data using the tabledata.insertAll endpoint:

curl -X POST \ -H “Authorization: Bearer YOUR_ACCESS_TOKEN” \ -H “Content-Type: application/json” \ -d ‘{“rows”: [{“json”: {“campaign_name”: “Summer Sale”, “impressions”: 12000, “clicks”: 950, “spend”: 340.75, “date”: “2025-07-15”}}]}’ \ “https://bigquery.googleapis.com/bigquery/v2/projects/YOUR_PROJECT_ID/datasets/YOUR_DATASET_ID/tables/YOUR_TABLE_ID/insertAll”

Swap in your own values for YOUR_ACCESS_TOKEN, YOUR_PROJECT_ID, YOUR_DATASET_ID, and YOUR_TABLE_ID.

4. Check the response for errors

A success looks like {“kind”: “bigquery#tableDataInsertAllResponse”}, while any failed rows come back listed with their row number and error type.Learn More: Airbyte vs Integrate.io: Which Is the Better Data Integration Tool?

Method #3: Sending Data to BigQuery from Facebook Ads Manually

In this method, you can download data from Facebook Ads and upload it to BigQuery manually without writing any code. However, you need to do this repeatedly whenever you need new data or run new campaigns.

Here are the steps to do it:

Step #1: Get Data from Facebook Ads

Follow the steps below to download data from Facebook Ads for the campaign you want to get data for:

• Open ‘Facebook Ads Manager’.
• Click ‘Create’ or ‘View a report’.
• Click ‘Export Table Data’ → Choose ‘CSV format’.
• Save the file to your computer.

Step #2: Create Google Cloud Project

Create a Google Cloud Project by following the steps below:

• Go to > https://console.cloud.google.com/ 
• Click ‘New Project’ → Give it a name → Click ‘Create’.

• Go to the ‘BigQuery’ section in the left-hand menu.
• Check for the project you created

Step 3: Upload Data to BigQuery

1. Go to BigQuery and select your project.
2. Click ‘+Create Dataset’ → Give it a name → Click ‘Create’.
3. Click on your dataset → Click ‘Create Table’.
4. In the Source section, select ‘Upload’ and choose the downloaded CSV file.
5. In the Destination section, select your dataset and name your new table.
6. In the Schema section, choose Auto-detect or enter manually.
7. Click ‘Create Table’.

The data you downloaded from Facebook Ads will be displayed on BigQuery.

Benefits of Sending Data From Facebook Ads to Google BigQuery

Sending Facebook Ads data to a data warehouse like BigQuery offers you diverse benefits, as explained below:

1. Identify Patterns with SQL Queries

Sending data to BigQuery from Facebook Ads lets you use advanced SQL queries to gain deeper insights into your ad performance. This helps you analyze data from multiple angles, spot patterns, and understand metric correlations.

2. Conduct Multi-channel Ad Analysis

Once you send your data to BiGQuery, you can integrate your Facebook Ads data with metrics from other sources, such as:

• Google Ads
• Google Analytics 4
• CRM or email marketing apps.

Doing this lets you analyze your overall marketing performance and understand how different channels work together.

3. Conduct In-depth Ad Performance Analysis

Using the data transferred from Facebook Ads, you can conduct a time-series analysis. This is a great way to identify changes in ad performance over time and to understand how factors like seasonality affect it.

4. Leverage ML Algorithms

You can also build and train ML models to forecast future performance using the Facebook Ads data in BigQuery. This also lets you identify which factors drive ad success, and optimize your campaigns accordingly.

5. Visualization of Your Campaign Data

​​You can build powerful interactive dashboards using the Facebook Ads data. All you need to do is connect BigQuery to visualization tools, like Power BI, Looker Studio, or another data visualization tool.

This enables you to create custom dashboards that showcase your key metrics, highlight trends, and provide actionable insights.

Using these insights, you can make informed, better decisions faster.

Why Integrate Facebook Ads Into BigQuery: 3 Use Cases

Integrating Facebook Ads into BigQuery is important for businesses in diverse ways.

Here are the top 3 use cases of integrating Facebook Ads with BigQuery:”

1. Run Efficient Marketing Campaigns

Analyzing Facebook Ads audience data in BigQuery can help you enhance the performance of your marketing campaigns.

Transferring the data to BigQuery lets you:

• Analyze the data in real-time to generate accurate insights
• Use the insights to understand how the audience responds to campaigns
• Create informed, data-driven decisions to improve campaign performance

This allows you to ensure every penny spent on the ad goes to help your business meet its campaign goals.

2. Improve Personalized Audience Targeting

You can analyze the Facebook Ads conversion data in BigQuery to segment the audience based on their:

• Detailed demographic
• Interests and preferences
• Behavior patterns

BigQuery’s powerful query capabilities can be used for this, improving personalized targeting and driving better conversions and ad performance.

3. Conduct Competitive Analysis

BigQuery lets you compare your Facebook attribution data to understand competitors’ ad performance.

You can do this by comparing your ad performance with competitors’ using publicly available data sources. This allows you to determine whether you meet the industry performance benchmarks.

Based on the performance insights, you can also optimize the ads to improve performance and outperform industry benchmarks.

Automate Facebook Ads Data Transfer to BigQuery Using Hevo

Getting Facebook Ads data into BigQuery is not really a matter of storage. The payoff comes when you combine that data with your CRM and product data, then run consistent queries and build reports that scale.

The trouble is, manual methods don’t scale, and custom code tends to break as soon as an API version retires or a schema shifts (something Meta is doing throughout 2026 as it deprecates older API versions). That’s where an automated ETL tool like Hevo shines. It handles extraction, loading, and reliability for you, so your team can spend its time on analysis instead of pipeline upkeep.

Here’s what makes Hevo a strong fit for syncing Facebook Ads to BigQuery:

• A no-code Facebook Ads connector that links your ad accounts without OAuth flows, API endpoints, or scheduled scripts
• Automatic schema handling, so a change on Facebook’s side doesn’t break your pipelines or queries
• Both incremental and historical syncs, which keep daily metrics current while still pulling in past performance, all without duplicates
• Built-in retries and error handling that smooth over API limits, token issues, and the occasional temporary failure
• Native loading into BigQuery, so your data is ready for SQL, dashboards, and models right away

If you want a low-maintenance way to keep Facebook Ads data flowing into BigQuery, try Hevo for free now.

Frequently Asked Questions