Facebook has over 2.7 billion monthly active users. This makes it a great source of big data. From users’ personal information like names, location, education, the pages they like, the ads they click, the posts they comment on, and more, data about users is constantly created and collected. 

This data is a rich source of insights and it can help Facebook and marketers to make sound decisions. For instance, this data is useful for running data-driven ads. Facebook data engineers normally develop tools, frameworks, and infrastructure to help them extract insights from this data and transform it into actions. This has a great impact on stuff like product decisions and it can affect millions of users globally.

In this article, you will be looking at the Data Engineering Facebook field in great detail. It covers the basics of Facebook Graph API and capturing content from Facebook. It then wraps up with the limitations of the Data Engineering Facebook field.  


This is what you need for this article:

  • An Active Facebook Account. 

Introduction to Facebook Graph API

Facebook data mining has been very useful in the past few years. The scraped or crawled data is constructive and valuable for scientific, commercial, and other fields of analysis and prediction, especially after deeper processing of the data. 

The Facebook Graph API is the primary way of scraping data from Facebook. It can also be used to get data into Facebook. It is an HTTP-based API used by apps to programmatically query data, manage ads, upload photos, post new stories, and perform many other tasks. 

The Graph API uses the idea of a “social graph”, which is a representation of information on Facebook. It is made up of the following:

  • Nodes: which are individual objects like a user, a photo, a comment, or a page. 
  • Edges: These are the connections between many objects and a single object, like comments on a photo or photos on a page. 
  • Fields: This is data about a particular object, like the name of a page and the birthday of a user. 

The nodes are used to get data about a particular object, edges give collections of objects on one object, while fields give data about a single object or every object in a particular collection. 

Since the Graph API is HTTP-based, it can work with any programming language that has an HTTP library, like urllib or cURL. This means that Graph API can be used directly on the web browser. 

There are many versions of the Graph API. This helps developers to know the times when the API will change. It takes about 2 years for a new version to be invented. This means that there is ample time for developers and apps to use any version of the API. The current version of Graph API is v11.0. Note that you can make a call to a particular version of the API. 

Understanding the Process of Capturing Content from Facebook

The simplest way for users to access their Facebook data is by using the Facebook API. You can access this using your web browser. After querying or calling the API, you will get formatted results that you can paste into documents and save them for analysis. 

To access the API, follow the steps given below.

Data Engineering Facebook: Logging to Facebook Account

Ensure that you are logged into your Facebook account and then click here or open the following link on the web browser:


Data Engineering Facebook: Choosing the Tools

Next, click the “More” tab and choose “Tools”. 

Facebook Developers Tools
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Data Engineering Facebook: Using Graph API Explorer

On the Developer Tools window, click “Graph API Explorer”. 

Developer Tools Window
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Data Engineering Facebook: Calling the API

The API interface will be opened. To be able to call the API, you must ensure that it has permission, that is, an access token to access your account. If you don’t find the access token in the “Access Token” field, click the “Generate Access Token” button to generate one. 

Generate Access Token
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Data Engineering Facebook: Populating the Access Token Box

On the window that pops up, configure all the settings and click the “Get Access Token” button. The Access Token Box will be populated. 

Data Engineering Facebook: Finding Your User ID

Find your User ID. Before you can use the API to make calls, you should query the API to get your user ID. You can find the ID immediately when you visit the API page. It will be of the form me?fields=id,name. Click the “Submit” button to complete the call to get the ID. 

Data Engineering Facebook: Making More Calls

Make more calls. You should make a note of the user ID and enter it and the query into the query box. Note that you can either enter the query manually or copy it from other sections and paste it into the query box. 

Graph API Explorer
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The GET button above the query box shows that your goal is to get information from Facebook. If you have the right permissions, you can still use the API to post information to Facebook as well as delete information from it. 

Data Engineering Facebook: Saving Calls

After calling content using the Facebook API, you should save it. You should manually copy the results of the API call and paste them into a separate document like a .txt document. Note that you are only allowed to copy and paste one-page results at a time. 

Limitations of Data Engineering Facebook

The following are the challenges of Facebook Data Engineering using the Facebook Graph API:

  • Time-Consuming: One has to go through a long process to start using Facebook Graph API to scrape data from Facebook. It involves generating an access token, getting your user id, and sometimes requesting permission from Facebook. 
  • Not Suitable for Non-Technical Users: The reason is that technical know-how is needed for one to write the queries. 
  • Access to Real-Time Data: Data engineers experience challenges when they need to scrape Facebook data in real-time using the Facebook Graph API. 


This blog talks about the Facebook Graph API in great detail while eliciting its key features, usage, and the limitations of leveraging Facebook Graph API for your business use case.

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Nicholas Samuel
Technical Content Writer, Hevo Data

Skilled in freelance writing within the data industry, Nicholas is passionate about unraveling the complexities of data integration and data analysis through informative content for those delving deeper into these subjects. He has written more than 150+ blogs on databases, processes, and tutorials that help data practitioners solve their day-to-day problems.

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