How To Send Data From Facebook Ads to BigQuery?

Extracting insights from Facebook Ads can be a struggle – limited exports, manual work, and siloed data often block the way. Google BigQuery offers a powerful solution, but getting the data there isn’t always straightforward. In this blog, we’ll explore three practical methods to integrate Facebook Ads with BigQuery –

• Using Hevo Data – an automated data pipeline platform
• Writing customer scripts, and
• Moving Data Manually

Let’s get started!

Method 1: Using Hevo to Move Data from Facebook Ads to BigQuery

Step 1.1: Connect Facebook Ads Account As Your Source

Step 1.2: Configure Google BigQuery as your Destination

That is all you need to do. Now, you can relax while your data automatically moves after setting up Facebook Ads BigQuery Integration.

Method 2: Writing Custom Scripts to Move Data

Step 2.1: Extract your Facebook Ads Data

You can pull Facebook Ads data using:

1. APIs – Use the Facebook Marketing API (RESTful) via SDKs in Python, PHP, JavaScript, R, or Ruby.
2. Real-time Streams – Subscribe to updates and stream data into a data warehouse.

Step 2.1.1: Extract Facebook Ads Data Through APIs

Use Facebook’s Graph API to fetch ads and insights. Example:

curl -F 'level=campaign' -F 'fields=[]' -F 'access_token=<ACCESS_TOKEN>' \
https://graph.facebook.com/v2.5/<CAMPAIGN_ID>/insights

Data will be available in CSV/XLS at:

https://www.facebook.com/ads/ads_insights/export_report?report_run_id=<REPORT_ID>
&format=<REPORT_FORMAT>&access_token=<ACCESS_TOKEN

Step 2.1.2: Extract via Real-time Streams

Subscribe to Facebook’s real-time updates to continuously stream ad data into your database. While real-time analytics is powerful, handling large-scale data requires robust protocols.

Step 2.2: Load Data Into BigQuery

1. Using Google Cloud Storage – Upload extracted data via:

• Console
• JSON API
• HTTP POST (Example using curl):

POST /upload/storage/v1/b/myBucket/o?uploadType=media&name=TEST HTTP/1.1
Host: www.googleapis.com
Content-Type: application/text
Authorization: Bearer your_auth_token

2. Directly into BigQuery – Use BigQuery APIs to send data via HTTP POST.

Once in Google Cloud Storage, create and run a LoadJob to import data into BigQuery.

Limitations of Using Custom Scripts

Building a custom code for transferring data from Facebook Ads to Google BigQuery may appear to be practical. However, this approach comes with some limitations, too.

• Code Maintenance: Since you are building the code yourself, you must also monitor and maintain it. If Facebook refreshes its API or the API sends a field with a datatype that your code doesn’t understand, you would need to have resources that can handle these ad-hoc requests.
• Data Consistency: You will also need to set up a data validation system to ensure no data leakage in the infrastructure.
• Real-time Data: The above approach can help you move data from Facebook Ads into BigQuery once. If you are looking to analyze data in real-time, you will need to deploy additional code on top of this.
• Data Transformation Capabilities: Often, you will need to transform the data received from Facebook before analyzing it. For example, when running ads across different geographies globally, you will want to convert the timezones and currencies from your raw data and bring them to a standard format. This would require extra effort.

Method 3: Manual Upload of Data from Facebook Ads to BigQuery

This is an affordable solution for moving data from Facebook Ads into BigQuery.

• Step 3.1: Create a Google Cloud project, after which you will be taken to a “Basic Checklist.” Next, navigate to Google BigQuery and look for your new project.
• Step 3.2: Log In to Facebook Ads Manager and navigate to the data you wish to query in Google BigQuery. If you need daily data, you need to segment your reports by day.
• Step 3.3: Download the data by selecting “Reports” and then click  “Export Table Data”. Export your data as a .csv file and save it on your PC.

• Step 3.4: Navigate back to Google BigQuery and ensure your project is selected at the top of the screen. Click on your project ID in the left-hand navigation and click on “+ Create Dataset.”

• Step 3.5: Provide your dataset’s name and ensure an encryption method is set. Click “Create Dataset,” followed by clicking on the name of your new dataset in the left-hand navigation. Next, click “Create Table” to finish this step.

• Step 3.6: Go to the source section, then create your table from the Upload option. Find your Facebook Ads report that you saved to your PC and choose file format as CSV. In the destination section, select “Search for a project.” Next, find your project name from the dropdown list. Select your dataset name and the name of the table.
• Step 3.7:  Navigate to the schema section and either select the checkbox to allow BigQuery to auto-detect the schema, or click on ‘Edit as Text’ to manually define the schema, including the name, mode, and type.

• Step 3.8: Go to the Partition and Cluster Settings section and choose either ‘Partition by Ingestion Time’ or ‘No Partitioning’ based on your requirements. Partitioning divides your table into smaller segments, enabling faster querying of specific data sections. Next, navigate to the Advanced Options and set the field delimiter, such as a comma.

• Step 3.9: Click “Create table.” Your Data Warehouse will begin to populate with Facebook Ads data. You can check your job history to see the status of your data load. Navigate to Google BigQuery and click on your dataset ID.
• Step 3.10: You can write SQL queries against your Facebook data in Google BigQuery, or export your data to Google Data Studio and other third-party tools for further analysis. To ensure fresh data availability, you can repeat this process for all additional Facebook data sets you wish to upload.

Limitations of Manual Upload of Data

• Data Extraction: Downloading data from Facebook Ads manually for large-scale data is daunting and time-consuming.
• Data Uploads: A manual process of uploading will need to be watched and involved continuously.
• Human Error: In a manual process, errors such as mistakes in data entry, omitted uploads, and duplication of records can take place.
• Data Integrity: There is no automated assurance mechanism to ensure the integrity and consistency of the data.
• Delays: Manual uploads risk creating delays in availability and the actual integration of data for analysis.

How To Prepare Your Facebook Ads Data for Google BigQuery

Before diving into the methods that can be deployed to set up a connection, you should ensure that your Facebook Ads data is furnished in an appropriate format. For instance, if the API pulls data and returns an XML file, you first have to transform it to a serialization that can be understood by BigQuery. As of now, the following two data formats are supported:

• JSON
• CSV

Apart from this, you also need to ensure that the data types you leverage are the ones supported by Google BigQuery, which are as follows:

FLOAT

STRING

FLOAT

RECORD

TIMESTAMP

INTEGER

Benefits of Sending Data From Facebook Ads to Google BigQuery

• Identify patterns with SQL queries: You can 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.
• Conduct multi-channel ad analysis: You can integrate your Facebook Ads data with metrics from other sources like 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.
• Analyze ad performance in-depth: You can conduct a time series analysis to identify changes in ad performance over time and understand how factors like seasonality impact ad performance.
• Leverage ML algorithms: You can also build and train ML models to forecast future performance, identify which factors drive ad success, and optimize your campaigns accordingly.
• Data Visualization: ​​You can build powerful interactive dashboards by connecting BigQuery to PowerBI, Looker Studio (former Google Data Studio), or another data visualization tool. This enables you to create custom dashboards that showcase your key metrics, highlight trends, and provide actionable insights to drive better marketing decisions.

Use Cases of Facebook Ads BigQuery Integration

• Marketing Campaigns: Analyzing Facebook Ads audience data in BigQuery can help you enhance the performance of your marketing campaigns. Leveraging Audience Insights enables marketers to create more targeted and effective campaigns by understanding detailed demographic and behavioral patterns. Advertisement data from Facebook combined with business data in BigQuery can give better insights for decision-making. 
• Personalized Audience Targeting: On viewing Facebook Ads conversion data in BigQuery, you can utilize BigQuery’s powerful querying capabilities to segment audiences based on detailed demographics, interests, and behaviors extracted from Facebook Ads data. 
• Competitive Analysis: You can compare your Facebook attribution data in BigQuery to understand the ad performance of industry competitors using publicly available data sources.

How Can You Get Real-time Streams of Your Facebook Ad Statistics?

You can easily create a real-time data infrastructure for extracting and loading data from Facebook Ads into a Data Warehouse repository. You can achieve this by subscribing to real-time updates to receive API updates with Webhooks. Armed with the proper infrastructure, you can have an almost real-time data feed into your repository and ensure that it will always be updated with the latest data. Facebook Ads is a real-time bidding system where advertisers can compete to showcase their advertising material.

Facebook Ads imparts a very rich API that allows you to get extremely granular data regarding your accounting activities and leverage it for reporting and analytic purposes. This richness will cost you, though many complex resources must be tackled with an equally intricate protocol.

You can also look at how to connect Facebook Ads to Snowflake to load your Facebook Ads data to different destinations.

Conclusion

This blog talks about three methods you can use to move data from Facebook Ads to BigQuery seamlessly. It also provides information on the limitations of manual methods and use cases of integrating Facebook Ads data to BigQuery.

Data migration from Facebook Ads is a time-consuming and tedious operation, but with the help of a data integration solution like Hevo, it can be done with little work and in no time.

Sign up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.

FAQ about Facebook Ads to Google BigQuery