How To Send Data From Facebook Ads to BigQuery?

Leveraging the data from Facebook Ads Insights offers businesses a great way to measure their target audiences. However, transferring massive amounts of Facebook ad data to Google BigQuery is no easy feat. In this article, you can take a look at how you can easily migrate data from Facebook Ads to BigQuery. 

Introduction to Facebook Ads

Facebook Ads is one of the most influential ad platforms in the contemporary world. It aids businesses in targeting and engaging audiences across the vast network provided by Facebook, including Instagram and Messenger. The platform utilizes extensive data about users and advanced options for targeting to create highly personalized ads that can reach out to specific demographics based on interests, behaviors, and locations.

Use Cases of Facebook Ads

• Driving Website Traffic: You can direct users to your website to increase visits and engagement.
• Promoting Products or Services: You can showcase and advertise specific products or services to potential customers.
• Generating Leads: You can capture contact information and generate potential customer leads through targeted ads.

You can also take a look at how you can work with Facebook Ad Creator tools for advanced use cases.

Introduction to Google BigQuery

BigQuery is Google’s cloud enterprise data warehouse that primarily serves business agility in running complex SQL queries and efficiently analyzing large datasets. It is based on Google technology called Dremel, using columnar storage and tree architecture to support high-speed data scanning for querying efficiency.

BigQuery is serverless, highly scalable, and enlists Google’s infrastructure in the cloud for management. It uses access patterns to maintain efficiency in storage by dynamically optimizing data structures to handle data in a changing environment for optimal performance.

Take a look at Google BigQuery in detail to understand how the platform works.

Use Cases of Google BigQuery

• Big Data Analytics: You can use BigQuery to process and analyze large datasets quickly.
• Data Warehousing: It is an easy-to-use and scalable data warehouse that you can use to store and transform large volumes of data.
• Business Intelligence(BI): It integrates with BI tools like Looker and Tableau, allowing you to create interactive dashboards and visualizations for in-depth business insights.

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:

• STRING
• FLOAT
• RECORD
• TIMESTAMP
• INTEGER
• FLOAT

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

Pulling data from Facebook involves downloading the relevant Ads Insights data, which can be used for various business purposes. Currently, there are two main methods for users to pull data from Facebook:

• Through APIs.
• Through Real-time streams.

Step 2.1.1: Extract Facebook Ads Data Through APIs

Users can access Facebook’s APIs through the different SDKs the platform offers. While Python and PHP are the main languages supported by Facebook, it’s easy to find community-supported SDKs for languages such as JavaScript, R, and Ruby.

The Facebook Marketing API is relatively easy to use, so it can be harnessed to execute requests that are direct to specific endpoints. Also, since the Facebook Marketing API is a RESTful API, you can interact with it via your favorite framework or language.

Like everything else Facebook-related, ads and statistics data can be acquired through the Graph API, and any requests for statistics specific to particular ads can be sent to Facebook Insights. Facebook Insights will reply to such requests with more information on the queried ad object. 

If the above seems overwhelming, there’s no need to worry; let’s look at an example to help simplify things. Suppose you want to extract all stats relevant to your account. This can be done by executing the following simple request through curl: 

curl -F 'level=campaign' -F 'fields=[]' -F 'access_token=<ACCESS_TOKEN>' 
https://graph.facebook.com/v2.5/<CAMPAIGN_ID>/insights 
curl -G -d 'access_token=<ACCESS_TOKEN>' https://graph.facebook.com/v2.5/1000002 
curl -G -d 'access_token=<ACCESS_TOKEN>' https://graph.facebook.com/v2.5/1000002/insights

Once it’s ready, the data you requested will be returned in either CSV or XLS format, and you can access it via a URL such as the one below:

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 Facebook Ads Data Through Real-time Streams

You can also pull data from Facebook by creating a real-time data substructure and load your data into your data warehouse. To do this and receive API updates, you have to subscribe to real-time updates.

Using the right substructure, you’ll be able to stream almost real-time data feed to your database, which will keep you updated with the latest data. 

Facebook Ads boasts a rich API that allows users to extract even smaller portions of data regarding accounts and target audience activities. More importantly, this real-time data can be used for analytics and reporting. 

However, a minor consideration needs to be addressed. The resources become more complex as they grow, meaning you’ll need a complex protocol to handle them. It’s worth keeping this in mind as your data volume grows.

Moving on, the data you pull from Facebook can be in many different formats, yet BigQuery isn’t compatible with all of them. This means it’s in your best interest to convert data into a format supported by BigQuery after pulling it from Facebook.

Step 2.2: Loading Data Into BigQuery

If you use Google Cloud Storage to load data from Facebook Ads into BigQuery, you’ll need to first load the data into Google Cloud Storage. This can be done in one of a few ways. 

First and foremost, this can be done directly through the console. Alternatively, you can post data with the help of the JSON API. One thing to note here is that APIs play a crucial role in pulling data from Facebook Ads and loading data into Bigquery.

The simplest way to load data into BigQuery is by requesting HTTP POST using tools such as curl. Should you decide to go this route,  your POST request should look something like this:

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

And if you enter everything correctly, you’ll get a response that looks like this:

HTTP/1.1 200 Content-Type: application/json { "name": "TEST" }

However, remember that tools like curl are only useful for testing purposes. So, you’ll need to write specific codes to send data to Google if you want to automate the data-loading process.

This can be done in one of the following languages when using the Google App Engine to write codes:

• Python
• Java
• PHP
• Go

Apart from coding for the Google App Engine, the above languages can even be used to access Google Cloud Storage. 

Once you’ve imported your extracted data into Google Cloud Storage, you’ll need to create and run a LoadJob, which directs to the data that needs to be imported from the cloud and will ultimately load the data into BigQuery. This works by specifying source URLs that point to the queried objects.  

This method makes use of POST requests for storing data in the Google Cloud Storage API, from where it will load the data into BigQuery.

Another method to accomplish this is by posting a direct HTTP POST request to BigQuery with the data you’d like to query. While this method is very similar to loading data through the JSON API, it differs by using specific BigQuery end-points to load data directly. Furthermore, the interaction is simple and can be carried out via either the framework or the HTTP client library of your preferred language. 

Take a look at how to load data into Bigquery to migrate your Facebook Ads data seamlessly.

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.

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. 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