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."
First, determine where your Rocket.Chat instance stores its data. By default, Rocket.Chat uses MongoDB. You'll need access to the MongoDB instance where Rocket.Chat is hosted. Obtain the necessary credentials and connection details to connect to this database.
Use MongoDB's `mongoexport` tool to extract data from the Rocket.Chat database. You can export data in JSON or CSV format. For example, to export messages, run a command like:
```
mongoexport --db rocketchat --collection messages --out messages.json
```
This command exports the messages collection to a JSON file named `messages.json`.
Once you have the data in JSON or CSV format, you might need to preprocess it to ensure compatibility with DuckDB. This may involve cleaning the data, flattening nested JSON structures, or converting data types to match DuckDB's requirements.
If you haven't already, install DuckDB on your system. You can download it from the DuckDB website or use a package manager like `pip` for Python:
```
pip install duckdb
```
With DuckDB installed, create a new database file where you will store the Rocket.Chat data. You can do this using the DuckDB CLI or programmatically with a script. For example, to create a database named `chat_data.db`, use:
```python
import duckdb
con = duckdb.connect('chat_data.db')
```
Use DuckDB's capabilities to import data from the prepared file into the newly created database. DuckDB can read directly from CSV and JSON files. For example, to import a CSV file:
```python
con.execute("CREATE TABLE messages AS SELECT * FROM read_csv_auto('messages.csv')")
```
This command creates a `messages` table and populates it with data from `messages.csv`.
Finally, verify that the data has been successfully imported into DuckDB. Use SQL queries to inspect the data and ensure its integrity. For example:
```python
result = con.execute("SELECT COUNT(*) FROM messages").fetchall()
print(result)
```
This query counts the number of records in the `messages` table to confirm the import was successful.
By following these steps, you can efficiently transfer data from Rocket.Chat to DuckDB 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.
Rocket.Chat is a customizable open-source communications platform for organizations with high standards of data protection that enables communication through federation, and over 12 million people are using it for team chat, customer service, and secure files. Rocket.Chat is a free and open-source team chat collaboration platform that permits users to communicate securely in real-time across devices on the web. Rocket.Chat is a platform that develops internal and external communication within a controlled and secure environment.
Rocket.chat's API provides access to a wide range of data related to the chat platform. The following are the categories of data that can be accessed through the API:
1. Users: Information about users, including their name, email address, and profile picture.
2. Channels: Details about channels, including their name, description, and members.
3. Messages: Information about messages sent in channels or direct messages, including the text, sender, and timestamp.
4. Integrations: Details about integrations with other services, such as webhooks and bots.
5. Permissions: Information about user permissions, including roles and permissions granted to specific users.
6. Settings: Configuration settings for the Rocket.chat platform, including server settings and user preferences.
7. Analytics: Data related to platform usage, such as the number of active users and the most popular channels.
Overall, the Rocket.chat API provides a comprehensive set of data that can be used to build custom integrations and applications on top of the chat platform.
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:





