

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
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


"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."


“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.”

"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."
Ensure that you have MongoDB installed and running on your local machine or server. Also, make sure Node.js is installed, as you'll need it to write a script for the data transfer. Verify both installations by running `mongo --version` and `node --version` in your terminal or command prompt.
Create a JSON file containing the data you wish to move to MongoDB. This file should be properly formatted, with an array of JSON objects if you're inserting multiple documents. Save it with a `.json` extension, for example, `data.json`.
Open a terminal and create a new directory for your project. Navigate into this directory and run `npm init -y` to initialize a new Node.js project. This command will create a `package.json` file, which will manage your project's dependencies.
Inside your project directory, run the following command to install the MongoDB Node.js driver: `npm install mongodb`. This package will allow your Node.js script to interact with your MongoDB database.
Create a new JavaScript file, e.g., `importData.js`, in your project directory. In this file, write a script to read the JSON file and insert its contents into the MongoDB database. Here is a basic template to get you started:
```javascript
const fs = require('fs');
const { MongoClient } = require('mongodb');
async function importData() {
const uri = 'mongodb://localhost:27017'; // Replace with your MongoDB URI if different
const client = new MongoClient(uri);
try {
await client.connect();
const database = client.db('yourDatabase'); // Replace 'yourDatabase' with your database name
const collection = database.collection('yourCollection'); // Replace 'yourCollection' with your collection name
// Read JSON file
const data = JSON.parse(fs.readFileSync('data.json', 'utf-8'));
// Insert data into MongoDB
const result = await collection.insertMany(data);
console.log(`${result.insertedCount} documents were inserted.`);
} finally {
await client.close();
}
}
importData().catch(console.error);
```
Execute your Node.js script to move the data from the JSON file to MongoDB. Use the terminal to navigate to your project directory and run the command `node importData.js`. Monitor the console output to verify that the data insertion was successful.
Open your MongoDB client (such as MongoDB Compass or the mongo shell) and connect to your database. Check the specified collection to ensure that the data from your JSON file has been inserted correctly. You can use a simple query like `db.yourCollection.find()` to list all documents in the collection and verify their presence.
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.
JSON (JavaScript Object Notation) is a lightweight data interchange format that is easy for humans to read and write and easy for machines to parse and generate. It is a text format that is used to transmit data between a server and a web application as an alternative to XML. JSON files consist of key-value pairs, where the key is a string and the value can be a string, number, boolean, null, array, or another JSON object. JSON is widely used in web development and is supported by most programming languages. It is also used for storing configuration data, logging, and data exchange between different systems.
JSON File provides access to a wide range of data types, including:
- User data: This includes information about individual users, such as their name, email address, and account preferences.
- Product data: This includes information about the products or services offered by a company, such as their name, description, price, and availability.
- Order data: This includes information about customer orders, such as the products ordered, the order status, and the shipping address.
- Inventory data: This includes information about the stock levels of products, as well as any backorders or out-of-stock items.
- Analytics data: This includes information about website traffic, user behavior, and other metrics that can help businesses optimize their online presence.
- Marketing data: This includes information about marketing campaigns, such as email open rates, click-through rates, and conversion rates.
- Financial data: This includes information about revenue, expenses, and other financial metrics that can help businesses track their performance and make informed decisions.
Overall, JSON File provides a comprehensive set of data that can help businesses better understand their customers, products, and performance.
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: