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 logging into your Facebook account and navigating to the Facebook Page you manage. Use the Facebook Page Insights and the Export Data feature to download the data you need. Facebook allows you to export data in CSV format, providing options for Page data, Post data, and more. Choose the relevant data type and time range, and download the CSV file to your system.
Open the downloaded CSV file using a spreadsheet application like Microsoft Excel or Google Sheets. Review the columns and data to ensure it contains the necessary information you want to transfer to Weaviate. Clean the data by removing any irrelevant rows or columns, correcting any formatting issues, and ensuring data consistency.
Weaviate requires data in JSON format, so convert your cleaned CSV data into JSON. You can do this by writing a simple script in Python or using online tools that convert CSV to JSON. Ensure that each data entry corresponds to a JSON object, and maintain a clear schema for attributes like title, content, likes, comments, etc.
If you haven't already, set up a Weaviate instance. You can do this by running a local instance using Docker or deploying it on a cloud platform. Follow Weaviate's official documentation to configure the instance, ensuring it is ready to accept data inputs. Familiarize yourself with Weaviate's schema and object creation process.
In Weaviate, define a schema that matches the structure of your JSON data. The schema should include classes and properties that correspond to the attributes in your JSON file (e.g., posts, comments, likes). Use the Weaviate Console or API to create this schema, ensuring it aligns with your data's structure and intended queries.
Develop a script, preferably in Python, to ingest the JSON data into Weaviate. Use the Weaviate client library for Python to facilitate this process. The script should authenticate with your Weaviate instance, iterate over the JSON objects, and use the Weaviate API to create objects in your configured schema. Handle any errors and validate successful uploads by checking responses.
After uploading the data, verify its integrity by querying the Weaviate instance. Use the Weaviate Console or API to execute queries and ensure that the data is accurately represented in the database. Check for completeness and accuracy, and make any necessary adjustments to your script or schema based on the results. Regularly test and validate to ensure the data meets your needs.
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:
