Trusted by data-driven companies
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."
First, log in to your Airtable account and navigate to the base containing the data you want to export. Choose the appropriate table and click on "View" to access the data. Use the "Download CSV" option to export the table's data as a CSV file. Save this file to your local machine.
Ensure you have MongoDB installed on your local machine or have access to a remote MongoDB server. You can install MongoDB from the official MongoDB website. Once installed, start the MongoDB server using the `mongod` command for local installations. If you're using a remote server, ensure you have network access.
Open the CSV file using a spreadsheet application like Excel or a text editor. Inspect the data for any inconsistencies, special characters, or formatting issues. Make adjustments as necessary to ensure the data is clean and ready for import. Save your changes.
Use a script or an online tool to convert your CSV file to JSON format, which is the format MongoDB uses for data. You can write a simple Python script using the `pandas` library for this purpose:
```python
import pandas as pd
df = pd.read_csv('yourfile.csv')
df.to_json('yourfile.json', orient='records', lines=True)
```
This script reads your CSV and saves it as a JSON file with newline-delimited records.
Access your MongoDB instance via the `mongo` shell. Create a new database and collection where you will import the data. Use the following commands:
```shell
use yourDatabaseName
db.createCollection('yourCollectionName')
```
Replace `yourDatabaseName` and `yourCollectionName` with appropriate names for your data.
Use the `mongoimport` tool to import the JSON data into your MongoDB collection. Run the following command in your terminal:
```shell
mongoimport --db yourDatabaseName --collection yourCollectionName --file yourfile.json --jsonArray
```
Make sure you replace `yourDatabaseName`, `yourCollectionName`, and `yourfile.json` with your actual database name, collection name, and JSON file path. The `--jsonArray` flag is used if your JSON file is an array of objects.
Open the `mongo` shell and verify that the data has been imported correctly by running:
```shell
use yourDatabaseName
db.yourCollectionName.find().pretty()
```
This command will display all the records from your collection in a readable format. Check for data integrity and completeness.
By following these steps, you can manually move data from Airtable to MongoDB 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.
Airtable is a cloud collaboration service.
Airtable's API provides access to a wide range of data types, including:
1. Tables: The primary data structure in Airtable, tables contain records and fields.
2. Records: Each row in a table is a record, which contains data for each field.
3. Fields: Each column in a table is a field, which can contain various data types such as text, numbers, dates, attachments, and more.
4. Views: Airtable allows users to create different views of their data, such as grid view, calendar view, and gallery view.
5. Forms: Airtable also allows users to create forms to collect data from external sources.
6. Attachments: Users can attach files to records, such as images, documents, and videos.
7. Collaborators: Airtable allows users to collaborate with others on their data, with different levels of access and permissions.
8. Metadata: Airtable's API also provides access to metadata about tables, fields, and records, such as creation and modification dates.
Overall, Airtable's API provides a comprehensive set of data types and features for users to manage and manipulate their data in a flexible and customizable way.
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:
