Warehouses and Lakes
Finance & Ops Analytics

How to load data from Rocket.chat to BigQuery

Learn how to use Airbyte to synchronize your Rocket.chat data into BigQuery within minutes.

TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

  1. set up Rocket.chat as a source connector (using Auth, or usually an API key)
  2. set up BigQuery as a destination connector
  3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Rocket.chat

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.

What is BigQuery

BigQuery is an enterprise data warehouse that draws on the processing power of Google Cloud Storage to enable fast processing of SQL queries through massive datasets. BigQuery helps businesses select the most appropriate software provider to assemble their data, based on the platforms the business uses. Once a business’ data is acculumated, it is moved into BigQuery. The company controls access to the data, but BigQuery stores and processes it for greater speed and convenience.

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Prerequisites

  1. A Rocket.chat account to transfer your customer data automatically from.
  2. A BigQuery account.
  3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Rocket.chat and BigQuery, for seamless data migration.

When using Airbyte to move data from Rocket.chat to BigQuery, it extracts data from Rocket.chat using the source connector, converts it into a format BigQuery can ingest using the provided schema, and then loads it into BigQuery via the destination connector. This allows businesses to leverage their Rocket.chat data for advanced analytics and insights within BigQuery, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Rocket.chat as a source connector

1. First, you need to obtain the necessary credentials to connect your Rocket.chat source connector. This includes the server URL, username, and password.  
2. Once you have the credentials, log in to your Rocket.chat account and navigate to the Administration panel.  
3. In the Administration panel, click on the Integrations tab and select the Incoming Webhooks option.  
4. Create a new Incoming Webhook by clicking on the New Integration button and filling out the necessary information.  
5. After creating the Incoming Webhook, copy the Webhook URL provided by Rocket.chat.  
6. Now, go to your Airbyte dashboard and click on the Sources tab.  
7. Click on the Add Source button and select the Rocket.chat source connector.  
8. In the Rocket.chat source connector configuration page, paste the Webhook URL you copied earlier into the Webhook URL field.  
9. Enter your Rocket.chat username and password in the appropriate fields.  
10. Click on the Test button to ensure that the connection is successful.  
11. If the test is successful, click on the Save button to save the configuration.  
12. Your Rocket.chat source connector is now connected to Airbyte and ready to be used for data integration.

Step 2: Set up BigQuery as a destination connector

1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.

2. Scroll down until you find the "BigQuery" destination connector and click on it.

3. Click the "Create Destination" button to begin setting up your BigQuery destination.

4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.

5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.

6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.

7. Finally, review your settings and click the "Create Destination" button to complete the setup process.

8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.

9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.

10. Follow the prompts to enter your source credentials and configure your sync settings.

11. When you reach the "Destination" step, select your BigQuery destination from the dropdown menu and choose the dataset and table prefix you want to use.

12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery destination.

Step 3: Set up a connection to sync your Rocket.chat data to BigQuery

Once you've successfully connected Rocket.chat as a data source and BigQuery as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select Rocket.chat from the dropdown list of your configured sources.
  3. Select your destination: Choose BigQuery from the dropdown list of your configured destinations.
  4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
  5. Select the data to sync: Choose the specific Rocket.chat objects you want to import data from towards BigQuery. You can sync all data or select specific tables and fields.
  6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Rocket.chat to BigQuery according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your BigQuery data warehouse is always up-to-date with your Rocket.chat data.

Use Cases to transfer your Rocket.chat data to BigQuery

Integrating data from Rocket.chat to BigQuery provides several benefits. Here are a few use cases:

  1. Advanced Analytics: BigQuery’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Rocket.chat data, extracting insights that wouldn't be possible within Rocket.chat alone.
  2. Data Consolidation: If you're using multiple other sources along with Rocket.chat, syncing to BigQuery allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
  3. Historical Data Analysis: Rocket.chat has limits on historical data. Syncing data to BigQuery allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: BigQuery provides robust data security features. Syncing Rocket.chat data to BigQuery ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: BigQuery can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Rocket.chat data.
  6. Data Science and Machine Learning: By having Rocket.chat data in BigQuery, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While Rocket.chat provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to BigQuery, providing more advanced business intelligence options. If you have a Rocket.chat table that needs to be converted to a BigQuery table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a Rocket.chat account as an Airbyte data source connector.
  2. Configure BigQuery as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from Rocket.chat to BigQuery after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

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Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
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Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
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Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter

Connectors Used

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter

Connectors Used

Frequently Asked Questions

What data can you extract from Rocket.chat?

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 data can you transfer to BigQuery?

You can transfer a wide variety of data to BigQuery. This usually includes structured, semi-structured, and unstructured data like transaction records, log files, JSON data, CSV files, and more, allowing robust, scalable data integration and analysis.

What are top ETL tools to transfer data from Rocket.chat to BigQuery?

The most prominent ETL tools to transfer data from Rocket.chat to BigQuery include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • Talend Data Integration

These tools help in extracting data from Rocket.chat and various sources (APIs, databases, and more), transforming it efficiently, and loading it into BigQuery and other databases, data warehouses and data lakes, enhancing data management capabilities.