Summarize this article with:
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
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."
Begin by familiarizing yourself with Qualaroo’s data export options. Qualaroo allows you to export survey data in formats such as CSV or JSON. Access the Qualaroo dashboard, navigate to the survey results section, and identify the export options available. Ensure you have the necessary permissions to export data.
Use the Qualaroo interface to manually export the data. Depending on the format you choose (e.g., CSV or JSON), download the file to your local system. Ensure the data is structured and organized in a manner that will be conducive to parsing and processing later.
If you haven't already, install the Google Cloud SDK on your local machine. This toolkit will allow you to interact with Google Cloud services. Follow the official installation guide provided by Google, ensuring you configure the SDK with your Google Cloud account credentials.
Log in to your Google Cloud Console and navigate to the Pub/Sub section. Create a new topic that will be used to receive the data from Qualaroo. Note the topic name as you will need it in subsequent steps to publish messages to this topic.
Develop a script in a programming language of your choice (e.g., Python) to read the exported Qualaroo data file. The script should parse the file contents and format the data into messages that are suitable for Pub/Sub. Ensure each message encapsulates a logical unit of data that you wish to publish.
Enhance your script to include functionality that uses the Google Cloud Pub/Sub client library to publish each formatted message to the Pub/Sub topic you created. Ensure the script handles authentication using the credentials configured with the Google Cloud SDK. Test the script with a small set of data to confirm that messages are published successfully.
To automate the data transfer process, consider setting up a scheduled task or cron job on your local machine. This task should run your script at defined intervals to check for new data exports from Qualaroo, transform them, and publish them to Pub/Sub. Ensure error logging and notifications are integrated to monitor the process for any issues.
By following these steps, you will be able to effectively transfer data from Qualaroo 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.
Qualaroo is a SaaS product that helps companies gather customer insights to grow their business. Koala's mission is to help companies understand the reasons behind their customers' and prospects' decisions. Understanding why leads to better business results like increasing sales, improving web conversion rates and experience, increasing product engagement, reducing churn, and more. Qualaroo makes it possible to intelligently target interactions by time on page, pages visited, number of site visits, source citations, or any internal data.
Qualaroo's API provides access to various types of data related to user feedback and behavior. The categories of data that can be accessed through Qualaroo's API are:
1. Survey data: This includes data related to the surveys created using Qualaroo, such as survey responses, completion rates, and survey questions.
2. User behavior data: This includes data related to user behavior on a website or application, such as page views, clicks, and time spent on a page.
3. User feedback data: This includes data related to user feedback, such as comments, ratings, and suggestions.
4. Demographic data: This includes data related to user demographics, such as age, gender, location, and occupation.
5. Conversion data: This includes data related to user conversions, such as conversion rates, conversion funnels, and revenue generated.
6. A/B testing data: This includes data related to A/B testing, such as test results, variations, and statistical significance.
Overall, Qualaroo's API provides access to a wide range of data that can help businesses better understand their users and improve their products and services.
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:
