Summarize this article with:
.png)
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."
Start by logging into your ConvertKit account using your credentials. Ensure you have the appropriate permissions to access and export subscriber data.
Once logged in, go to the "Subscribers" section. This is where you can view and manage your list of email subscribers. You'll find this option in the main dashboard menu.
Decide if you need to export all subscribers or a specific segment. Use the filtering options to select the desired group. You can filter by tags, custom fields, or other criteria available in ConvertKit.
Look for the "Export" option, typically located at the top-right corner of the subscribers list. Click on it to initiate the export process. ConvertKit will prepare a CSV file for download containing the subscriber data based on your selections.
After the export is prepared, ConvertKit will send you an email with a download link, or you may be able to download it directly from the dashboard. Click the link to download the CSV file to your local computer.
Open the downloaded CSV file using a spreadsheet application like Excel or Google Sheets. Verify that the data is complete and correctly formatted. Check for any missing fields or discrepancies that might require attention.
Finally, ensure that the CSV file is securely stored on your local system. Consider using secure file storage practices, including backups and encryption, if the data is sensitive. This step is crucial to protect subscriber information and comply with data protection regulations.
By following these steps, you can effectively transfer your subscriber data from ConvertKit to a local CSV file without relying on third-party tools.
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.
ConvertKit is basically an email marketing platform for professional bloggers. ConvertKit assists you to increase and monetize your audience with ease. It helps you connect with your audience and increase your business using email marketing software that is so easy to use you can spend less time in our tool and more time creating. ConvertKit is an email marketing and email newsletter platform for capturing leads from your WordPress blog.
ConvertKit's API provides access to a wide range of data related to email marketing campaigns. The following are the categories of data that can be accessed through ConvertKit's API:
1. Subscribers: This category includes data related to subscribers such as their email address, name, location, and subscription status.
2. Forms: This category includes data related to forms such as form ID, name, and the number of subscribers who have signed up through the form.
3. Tags: This category includes data related to tags such as tag ID, name, and the number of subscribers who have been tagged.
4. Sequences: This category includes data related to sequences such as sequence ID, name, and the number of subscribers who have been added to the sequence.
5. Broadcasts: This category includes data related to broadcasts such as broadcast ID, name, and the number of subscribers who have received the broadcast.
6. Automations: This category includes data related to automations such as automation ID, name, and the number of subscribers who have been added to the automation.
7. Metrics: This category includes data related to metrics such as open rates, click-through rates, and conversion rates for email campaigns.
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:
