Trusted by data-driven companies
Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
Chase Zieman
“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
Start by creating a Facebook Developer account if you haven't already. This is necessary to access the Facebook Graph API. Go to the Facebook Developers website, sign in with your Facebook account, and follow the instructions to set up your developer profile.
After setting up your developer account, create a new Facebook App. This app will be used to generate the access tokens required to access the Facebook Graph API. Go to the "My Apps" section, click "Create App," and follow the prompts to create the app. Choose the "Business" option if prompted, as it provides access to page data.
Once your app is set up, navigate to the "Tools" section to generate access tokens. You will need a Page Access Token to access page-specific data. Use the Graph API Explorer tool, select your app, and generate a Page Access Token by selecting the necessary permissions such as `pages_read_engagement` and `pages_read_user_content`.
Determine what data you want to move from Facebook Pages. Common data includes posts, comments, likes, and user interactions. Use the Facebook Graph API documentation to identify the endpoints and fields that correspond to the data you need. For instance, use `/page-id/posts` to fetch posts from a specific page.
Write a script in a language like Python to make HTTP requests to the Facebook Graph API using the access token. Use libraries such as `requests` to handle HTTP requests. For example, to fetch posts, make a GET request to the endpoint `https://graph.facebook.com/v11.0/{page-id}/posts?access_token={access-token}`. Parse the JSON response to extract the desired data.
Install PostgreSQL on your server or local machine if it's not already set up. Configure your database by creating a new database and defining tables that match the structure of the data you intend to store. Use tools like `psql` or a graphical tool like pgAdmin to create tables with appropriate columns for storing Facebook data.
Connect to your PostgreSQL database using a library such as `psycopg2` for Python. Use SQL `INSERT` statements to add the fetched data into your tables. Ensure you handle data types correctly and manage exceptions, such as duplicate entries or connection errors, to ensure data integrity. For example, if you're storing posts, execute an `INSERT INTO posts (id, message, created_time) VALUES (%s, %s, %s)` statement for each post.
By following these steps, you can manually move data from Facebook Pages to a PostgreSQL database without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Facebook Pages permits businesses to promote their brand, grow their audience and start conversations with customers and people interested in learning more. A Facebook Page is where customers go to discover and engage with your business. Setting up a Page is simple and free, and it looks great on both desktop. A Facebook page is a public profile specifically created for businesses, brands, celebrities, causes, and other organizations. It provides a way for businesses and other organizations to interact with rather than just advertise to potential.
The Facebook Pages API provides access to a wide range of data related to Facebook Pages. The following are the categories of data that can be accessed through the API:
1. Page Information: This includes basic information about the page such as name, category, description, and contact information.
2. Posts: This includes all the posts made by the page, including status updates, photos, videos, and links.
3. Comments: This includes all the comments made on the page's posts.
4. Reactions: This includes the number of likes, loves, wows, hahas, sads, and angries on the page's posts.
5. Insights: This includes data related to the page's performance, such as reach, engagement, and follower demographics.
6. Messages: This includes all the messages sent to the page by users.
7. Reviews: This includes all the reviews left by users on the page.
8. Events: This includes all the events created by the page.
9. Videos: This includes all the videos uploaded by the page.
10. Photos: This includes all the photos uploaded by the page.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
