Summarize this article with:


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."
Before transferring data, ensure your Excel file is well-organized. Remove unnecessary columns and clean up any missing or erroneous data. Make sure the data is structured in a way that aligns with how you want it to appear in Convex.
Excel files need to be converted to a format that can be easily processed by scripts. Open your Excel file, go to 'File', select 'Save As', and choose 'CSV (Comma delimited) (*.csv)' as the format. Save the file to a known location on your computer.
Convex applications are written in JavaScript, so you'll use Node.js to write a script for transferring data. Download and install Node.js from its official website (https://nodejs.org/). Follow the installation instructions for your operating system.
Create a new directory for your project and navigate to it in your terminal. Initialize a new Node.js project by running `npm init -y`. Create a new file named `transferData.js`. In this file, write a script to read the CSV file using Node.js's `fs` module and the `csv-parser` package to parse the data.
```javascript
const fs = require('fs');
const csv = require('csv-parser');
fs.createReadStream('yourdata.csv')
.pipe(csv())
.on('data', (row) => {
// Process the row data here
console.log(row);
})
.on('end', () => {
console.log('CSV file successfully processed');
});
```
Initialize a new Convex project if you haven't already. Follow the Convex documentation to set up a new project and configure your environment. Ensure your Convex project is running locally or accessible via the Convex platform.
Modify your Node.js script to send data to your Convex database. Use Convex's HTTP API or client libraries to write the data. If using HTTP, you'll use `fetch` or a similar package to send POST requests with the CSV data to Convex endpoints.
```javascript
const fetch = require('node-fetch'); // Install node-fetch if not available
fs.createReadStream('yourdata.csv')
.pipe(csv())
.on('data', async (row) => {
try {
const response = await fetch('https://your-convex-endpoint.com/api/data', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify(row),
});
const data = await response.json();
console.log('Data sent to Convex:', data);
} catch (error) {
console.error('Error sending data:', error);
}
})
.on('end', () => {
console.log('CSV file successfully processed and data sent to Convex');
});
```
After the script has completed, verify that the data has been successfully transferred to your Convex database. Use the Convex dashboard or query tools to inspect the data, ensuring it matches what was in your original Excel file.
By following these steps, you can transfer data from an Excel file to Convex manually without relying on third-party connectors or integrations.
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.
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:





