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, you need to export the data from Rocket.Chat. Log into your Rocket.Chat instance with appropriate administrative privileges. Navigate to the Administration section and look for data export options. You may need to export chat data as a CSV or JSON file, depending on the available formats. Make sure to save this file to a location you can easily access.
After exporting the data, open the file you saved. If the data is in JSON format, convert it to CSV. This can be done using a text editor or a script to parse the JSON and restructure it into comma-separated values. Ensure the CSV file has clear headers and data is structured in a way that matches your desired layout in Google Sheets.
Log into your Google account and open Google Sheets. Create a new spreadsheet or open an existing one where you want to import the Rocket.Chat data.
In your Google Sheet, go to the "File" menu, select "Import," then choose the "Upload" tab. Click "Select a file from your device" and navigate to the CSV file you prepared. Google Sheets will prompt you with import options. Choose to replace the current sheet or insert new sheets as needed, and ensure you select the correct delimiter if your CSV uses something other than commas.
Once the data is imported, you may need to format it for better readability. Adjust column widths, apply text wrapping, and use bold headers. You can also use Google Sheets' built-in tools to apply conditional formatting or create filters to make the data easier to analyze.
Go through the imported data to ensure all entries have been correctly imported and formatted. Check for any discrepancies or errors that may have occurred during the export-import process. If you notice missing or misaligned data, re-check your CSV file and re-import if necessary.
Although you're not using third-party connectors, you can automate future imports by creating a script in Google Apps Script. Write a script that accesses your Rocket.Chat data via its API, formats it as needed, and inserts it directly into Google Sheets. This requires JavaScript knowledge and access to Rocket.Chat's API documentation but will save time for regular data updates.
By following these steps, you can effectively transfer data from Rocket.Chat to Google Sheets manually, maintaining control over the entire process without relying on external services.
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:
