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."
First, ensure you have access to the Facebook Marketing API. You need to create a Facebook App through the Facebook Developer portal. Once your app is created, generate an access token with the required permissions to access your marketing data. This will allow you to programmatically retrieve data from your Facebook marketing account.
Use the Facebook Graph API to query and retrieve the marketing data you need. You can use programming languages like Python or JavaScript to send HTTP requests to the Graph API endpoints. For instance, you might want to retrieve ad insights, campaign data, or audience metrics. Ensure you format your API calls correctly and handle pagination if your data is extensive.
Once you have retrieved the data, extract the necessary fields and format them for Typesense. This involves cleaning and structuring the data into a JSON format that's compatible with Typesense. For example, if you're importing ad insights, you might extract fields like campaign name, impressions, clicks, etc., and structure these into a JSON document.
Set up a Typesense server where your formatted data will be stored. You can install Typesense on your local machine or a cloud server. Follow the official Typesense installation guide to ensure your server is configured correctly. Make sure to note down the server API key, host, and port for future use.
Before importing data, you need to define a schema for a collection in Typesense. This schema outlines the structure of the data you plan to import. It includes specifying fields, their types, and any additional options like faceting or sorting. Use the Typesense API or dashboard to create this schema, ensuring it matches the structure of your formatted data.
With your data formatted and schema defined, use the Typesense API to import your data. Send POST requests to the Typesense server with your JSON documents. Be sure to handle potential errors and verify that your data is correctly indexed in Typesense by querying the collection after import.
Once your data is imported, perform queries to ensure everything is indexed correctly. Check for completeness and accuracy. Additionally, optimize your Typesense setup by configuring indexing options, relevance tuning parameters, and ensuring your server is adequately resourced to handle search queries efficiently.
By following these steps, you can move data from Facebook Marketing to Typesense without relying on third-party connectors or integrations, ensuring a streamlined and controlled data transfer process.
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 Marketing is an extension of Facebook’s online social networking service. Making strategic use of its gigantic user base, Facebook has partnered with AXA Group to leverage the power of people connections (over 1.32 billion active users monthly) for extraordinarily efficient digital marketing and commercial collaboration. Through Facebook’s huge user base, Facebook Marketing is able to reach unprecedented numbers of people with personalized sales and marketing advertisements, making it a huge addition to the world of marketing.
Facebook Marketing's API provides access to a wide range of data that can be used for advertising and marketing purposes. The types of data that can be accessed through the API include:
1. Ad performance data: This includes metrics such as impressions, clicks, conversions, and cost per action.
2. Audience data: This includes information about the demographics, interests, and behaviors of the people who engage with your ads.
3. Campaign data: This includes information about the campaigns you have run, such as budget, targeting, and ad creative.
4. Page data: This includes information about your Facebook Page, such as the number of likes, followers, and engagement metrics.
5. Insights data: This includes data about how people are interacting with your content on Facebook, such as reach, engagement, and video views.
6. Custom audience data: This includes information about the custom audiences you have created, such as their size and composition.
7. Ad account data: This includes information about your ad account, such as billing and payment information.
Overall, the Facebook Marketing API provides a wealth of data that can be used to optimize your advertising campaigns and improve your marketing efforts on the platform.
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:
