Summarize


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."
Before moving data to Google Pub/Sub, ensure you understand how your data is being merged. Clearly define the format and structure of the merged data and how it is stored or accessed within your system. This is crucial for extracting the correct data for publishing.
If you haven't already, set up a Google Cloud Project. This involves creating a project in the Google Cloud Console, enabling billing, and setting up the Google Cloud SDK. Ensure that Pub/Sub is enabled for your project by navigating to the API library in the console and enabling the Pub/Sub API.
In the Google Cloud Console, create a new Pub/Sub topic. A topic is a named resource to which messages are sent by publishers. Navigate to the Pub/Sub section, click "Create Topic," and give it a unique name. Note this name as you will need it for the publishing process.
Set up authentication for your application to interact with Google Pub/Sub. This typically involves creating a service account in the Google Cloud Console, downloading the JSON key file, and setting the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to the path of this key file on your server or local machine.
Implement a method to extract the merged data from your system. This could be a script or a function depending on how your system is set up. Ensure the data is in a format that can be serialized into a message format for Pub/Sub, typically JSON or plain text.
Use the Pub/Sub client library for your programming language of choice to publish the extracted data to the topic you created. Initialize the Pub/Sub client, specify the topic, and use the `publish` method to send the data. Ensure you handle any exceptions or errors in the publishing process to maintain data integrity.
Example in Python:
```python
from google.cloud import pubsub_v1
publisher = pubsub_v1.PublisherClient()
topic_path = publisher.topic_path('your-project-id', 'your-topic-name')
data = 'Your merged data' # Ensure this is in bytes
data = data.encode('utf-8')
future = publisher.publish(topic_path, data)
print(f'Published message ID: {future.result()}')
```
After publishing, verify that your messages are being delivered to the topic by subscribing to it. You can create a subscription in the Google Cloud Console and pull messages to ensure they have been received. This step helps in confirming that the entire process is functioning as expected.
By following these steps, you can successfully move data from a merge operation to Google Pub/Sub 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.
Merge is a puzzle game where players combine matching blocks to create new ones and clear the board. The game starts with simple blocks, but as players progress, they encounter more complex shapes and colors. The goal is to merge as many blocks as possible to earn points and advance to higher levels. The game also includes power-ups and special blocks that can help players clear the board more quickly. Merge is a fun and addictive game that challenges players to think strategically and quickly to achieve high scores.
Merge's API provides access to a wide range of healthcare data, including:
1. Patient Data: This includes demographic information, medical history, and clinical notes.
2. Imaging Data: This includes medical images such as X-rays, CT scans, and MRIs.
3. Clinical Trial Data: This includes information on clinical trials, including study design, patient enrollment, and outcomes.
4. Medical Device Data: This includes data from medical devices such as pacemakers, insulin pumps, and blood glucose monitors.
5. Electronic Health Record (EHR) Data: This includes data from EHR systems, such as medication lists, lab results, and vital signs.
6. Genomic Data: This includes genetic information, such as DNA sequencing data and gene expression data.
7. Research Data: This includes data from research studies, such as survey data and clinical trial data.
Overall, Merge's API provides access to a comprehensive set of healthcare data, enabling developers to build innovative applications and solutions that improve patient care and outcomes.
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: