

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."
To begin, you need to access Trello's API. First, log into your Trello account and navigate to the Trello API documentation. Create an API key and token by following the instructions provided. Ensure you store these credentials securely, as you will use them to authenticate your API requests.
Use the Trello API to fetch data from your Trello boards. You can do this by sending a GET request to the Trello API endpoint for boards, lists, or cards, depending on the data you need. Use tools like `curl`, `Postman`, or write a simple script in Python or any language that supports HTTP requests. For example, in Python, you can use the `requests` library to make a GET request using your API key and token.
Once you receive the data from Trello, it will typically be in JSON format. Parse this JSON data to extract the specific fields you want to move to MongoDB. You may want to clean and structure the data at this stage, ensuring it's organized in a way that aligns with your MongoDB schema.
Ensure you have a MongoDB database set up where you can insert your Trello data. You can set this up locally or use a cloud-based MongoDB service like MongoDB Atlas. Create a new database and define collections that will store the Trello data, ensuring they have the appropriate structure to accommodate the parsed data from Trello.
Write a script to establish a connection to your MongoDB database. If you're using Python, you can use the `pymongo` library. Install this library using `pip install pymongo` if you haven't already. Use the connection string provided by your MongoDB instance, and authenticate if necessary, to establish a successful connection.
Use the parsed JSON data from Trello to insert documents into the MongoDB collections. Map each field from the Trello data to the appropriate field in your MongoDB collection. Use the MongoDB driver from your language of choice to perform the insertion. For Python and `pymongo`, you can use methods like `insert_one()` or `insert_many()` depending on the volume of data.
After the data insertion process is complete, verify that the data in your MongoDB collections accurately reflects the data from Trello. This can be done by querying the MongoDB database and comparing a sample of the data to ensure consistency. Also, check for any errors or discrepancies that may have occurred during the data transfer process.
By following these steps, you can manually move data from Trello to MongoDB without relying on third-party connectors or integrations, providing you with full control over the 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.
Trello is a web-based, Kanban-style, list-making application and is a subsidiary of Atlassian. Originally created by Fog Creek Software in 2011, it was spun out to form the basis of a separate company in 2014 and later sold to Atlassian in January 2017. The company is based in New York City.
Trello's API provides access to a wide range of data related to boards, cards, lists, members, and organizations. Here are the categories of data that Trello's API gives access to:
- Boards: Information about boards, including their name, description, URL, and members.
- Cards: Details about individual cards, such as their name, description, due date, and attachments.
- Lists: Information about lists, including their name, position, and cards.
- Members: Data related to members, such as their name, email address, and avatar URL.
- Organizations: Details about organizations, including their name, description, and members.
In addition to these categories, Trello's API also provides access to data related to actions, checklists, labels, and more. With this data, developers can build custom integrations and applications that interact with Trello in a variety of ways. For example, they can create custom reports, automate workflows, or build dashboards that display Trello data in real-time.
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:





