

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


"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."
Begin by exporting your data from Monday.com. Navigate to your desired board, click on the three-dot menu in the top right corner, and select "Export to Excel." This will download the board data as an Excel file, which you can then convert to a CSV file for easier processing.
Open the exported Excel file and save it as a CSV file. Ensure that the column headers are clean and appropriately named, as these will be used as field names in Typesense. Remove any unnecessary columns or rows to streamline the data import process.
Download and install Typesense on your server. You can do this by visiting the [Typesense GitHub repository](https://github.com/typesense/typesense) and following the installation instructions for your operating system. Ensure that the Typesense server is running and accessible.
Create a schema for your Typesense collection that matches the structure of your data. This involves specifying the fields and their types. For instance, create a JSON schema file like this:
```json
{
"name": "your_collection_name",
"fields": [
{"name": "id", "type": "int32"},
{"name": "name", "type": "string"},
{"name": "status", "type": "string"}
],
"default_sorting_field": "id"
}
```
Adjust the field names and types according to your data.
Use the Typesense API to create a new collection based on your schema. You can do this by sending a request to the Typesense server. Here�s an example using `curl`:
```bash
curl -X POST "http://localhost:8108/collections" \
-H "X-TYPESENSE-API-KEY: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d @your_schema_file.json
```
Write a script to read the CSV file and import the data into Typesense. You can use a programming language like Python. Here's an example snippet using Python and the `requests` library:
```python
import csv
import requests
api_key = 'YOUR_API_KEY'
typesense_host = 'http://localhost:8108'
collection_name = 'your_collection_name'
with open('your_data.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
response = requests.post(
f'{typesense_host}/collections/{collection_name}/documents',
headers={'X-TYPESENSE-API-KEY': api_key},
json=row
)
print(response.json())
```
Modify the script to match the structure of your data and your Typesense server configuration.
Once the data import is complete, verify that the data has been correctly added to your Typesense collection. You can do this by querying the Typesense collection through the API or using the Typesense Dashboard if available. Check for data accuracy and completeness.
By following these steps, you should be able to move data from Monday.com to Typesense effectively without using any 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.
Monday is the first day of the week in most countries and is typically associated with the start of a new work or school week. It is often viewed as a day of productivity and setting goals for the week ahead. Many people may feel a sense of dread or stress on Mondays, commonly referred to as the "Monday blues." However, others may view it as an opportunity to start fresh and tackle new challenges. Some cultures also have specific traditions or superstitions associated with Mondays, such as avoiding certain activities or wearing specific colors. Overall, Monday represents a new beginning and a chance to make the most of the week ahead.
Monday's API provides access to a wide range of data related to project management and team collaboration. The following are the categories of data that can be accessed through Monday's API:
1. Boards: This category includes data related to the boards created in Monday, such as board name, description, and status.
2. Items: This category includes data related to the items created within a board, such as item name, description, and status.
3. Users: This category includes data related to the users who have access to a board, such as user name, email address, and role.
4. Groups: This category includes data related to the groups created within a board, such as group name, description, and members.
5. Columns: This category includes data related to the columns created within a board, such as column name, type, and settings.
6. Updates: This category includes data related to the updates made to a board or item, such as update text, creator, and timestamp.
7. Notifications: This category includes data related to the notifications sent to users, such as notification type, recipient, and timestamp.
Overall, Monday's API provides access to a comprehensive set of data that can be used to build custom integrations and applications to enhance project management and team collaboration.
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: