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
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
"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"
“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.”
“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria. The value of being able to scale and execute at a high level by maximizing resources is immense”
- Login to your Chargebee account to access its web interface.
- Look at the right-hand navigation bar on the home page and click on Settings options.
- Click on the Import & Export Data option from the dropdown menu.
- You’ll see an Export Chargebee data section. There, click on the Export data button.
- Now, you can export the data you want, including invoice data, transactions, product catalog, and subscription data.
- After selecting the export data, click the Select Criteria to export button.
- Next, it'll give you a popup menu that previews data where you can add certain filters and conditions according to your business requirements. You can also choose to export data in default configurations.
- Lastly, click on the Export option, and it will start preparing your data and download it on a zip file on your local machine.
- Go to the zip file you downloaded on your local machine and extract it to obtain a CSV file with the data on it.
- Verify the data in the CSV file and structure it as per your ideal format if necessary.
- Now, we will use Google Drive as a mediator to upload the CSV file to BigQuery easily.
- To perform this task, log in to your Google Drive account.
- From the top left corner, click on the +New button. Then, from the dropdown menu, select File Upload.
- Choose the CSV file you downloaded from Step 1 and upload it.
- After the upload is successful, copy the link to the file destination in Google Drive.
- Login to your Google Cloud console and navigate to the BigQuery page.
- In the Explorer pane from the left side, create a new project or select an existing one in BigQuery.
- Click on the Create dataset option to create a new database under your current project.
- Now provide a unique Dataset name, and fill in other configuration details per your requirements. Click CREATE DATASET.
- Right-click on the Dataset you just created and click +Create table.
- In the Create table section, give your table a unique name.
- Select Drive as the source. In the Select Drive URI field, paste the link to the file you uploaded to Google Drive from Step 2, and set the file format to CSV.
- Configure other details and click CREATE TABLE.
- Ensure the destination is the project and database you created in BigQuery.
- Now, BigQuery will automatically import the data file you exported from Step 1.
- Verify the table's structure and tweak the formatting according to your requirements.
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.
Chargebee offers subscription and recurring billing system for subscription-based SaaS and eCommerce businesses. It is built with a focus on delivering the best experience to provide a seamless and flexible recurring billing experience to customers and manage customer subscriptions. With the subscription businesses expanding worldwide, eachrecurring revenue business needs more options and flexibility to manage varied billing use-cases.
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:
