

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


"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"


“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.”


“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria. The value of being able to scale and execute at a high level by maximizing resources is immense”
1. Install Node.js: Make sure you have Node.js installed on your machine, as you'll be using it to run scripts that interact with Firebase and MongoDB.
2. Install Firebase CLI: Install the Firebase CLI to interact with Firebase from the command line. You can install it via npm:
```
npm install -g firebase-tools
```
3. Install MongoDB: Ensure MongoDB is installed and running on your local machine or server. You can download it from the MongoDB official website.
4. Install MongoDB Driver: Install the official MongoDB Node.js driver to allow your script to interact with your MongoDB instance:
```
npm install mongodb
```
1. Authenticate Firebase CLI: Authenticate with Firebase using the CLI:
```
firebase login
```
2. Access Your Firebase Project: Navigate to your Firebase project directory or initialize a new one:
```
firebase init
```
3. Export Data: Export your Firebase Realtime Database data to a JSON file:
```
firebase database:get / > firebase-export.json
```
The exported data will be in JSON format. You may need to transform this data into a format that's suitable for MongoDB, especially if your data is deeply nested or not structured in the way MongoDB expects.
1. Write a Script to Transform Data: Create a Node.js script that reads the exported JSON file, transforms the data into the desired structure, and saves it to a new JSON file. Here's a simple example:
```javascript
const fs = require('fs');
let rawData = fs.readFileSync('firebase-export.json');
let firebaseData = JSON.parse(rawData);
// Transform the data here according to your needs
let transformedData = transformData(firebaseData);
fs.writeFileSync('transformed-data.json', JSON.stringify(transformedData));
function transformData(data) {
// Your transformation logic
return data;
}
```
1. Write a MongoDB Import Script: Create a script that reads the transformed JSON file and imports it into MongoDB.
```javascript
const MongoClient = require('mongodb').MongoClient;
const fs = require('fs');
let rawData = fs.readFileSync('transformed-data.json');
let dataToImport = JSON.parse(rawData);
const url = 'mongodb://localhost:27017';
const dbName = 'myDatabase'; // Replace with your database name
const collectionName = 'myCollection'; // Replace with your collection name
MongoClient.connect(url, { useNewUrlParser: true, useUnifiedTopology: true }, (err, client) => {
if (err) throw err;
console.log(""Connected to MongoDB!"");
const db = client.db(dbName);
const collection = db.collection(collectionName);
collection.insertMany(dataToImport, (err, result) => {
if (err) throw err;
console.log(`Inserted ${result.insertedCount} documents`);
client.close();
});
});
```
2. Run the Import Script: Execute your script to import the data into MongoDB.
```
node mongo-import.js
```
1. Check MongoDB: Use the MongoDB shell or a GUI tool like MongoDB Compass to verify that the data has been imported correctly.
2. Query the Data: Run some queries to ensure that the data looks right and is structured as you expect.
1. Remove Temporary Files: Once the import is verified, you can remove any temporary files that were created during the process, such as the exported JSON file from Firebase and any transformed data files.
2. Secure Your Data: Ensure that your MongoDB instance is secured and that proper access controls are in place to protect your data.
By following these steps, you should be able to successfully move data from Firebase Realtime Database to MongoDB without using third-party connectors or integrations. Remember to back up your data before performing operations like these to prevent any potential data loss.
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.
The Firebase Real-time Database allows you to build rich, collaborative applications by allowing secure access to the database directly from client-side code. The Firebase Real-time Database is a NoSQL database from which we can store and sync the data between our users in real-time. Firebase Real-time Database is a solution that stores data in the cloud and offers an easy way to sync your data among various devices, and it is a cloud-hosted database. Data is stored as JSON and synchronized in real-time to every connected client.
Firebase's API gives access to a wide range of data types, including:
1. Real-time database: This includes data that is stored in real-time and can be accessed and updated in real-time.
2. Cloud Firestore: This is a NoSQL document database that stores data in documents and collections.
3. Authentication: This includes user data such as email, password, and authentication tokens.
4. Cloud Storage: This includes data such as images, videos, and other files that are stored in the cloud.
5. Cloud Functions: This includes data that is processed by serverless functions in the cloud.
6. Cloud Messaging: This includes data related to push notifications and messaging.
7. Analytics: This includes data related to user behavior and app usage.
8. Performance Monitoring: This includes data related to app performance and user experience.
9. Remote Config: This includes data related to app configuration and feature flags.
Overall, Firebase's API provides access to a wide range of data types that are essential for building modern web and mobile applications.
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: