How to load data from ConvertKit to BigQuery

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

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Bespoke pipelines are:
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a ConvertKit connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted ConvertKit data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the ConvertKit to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

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Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

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What our users say

Raman Singh

Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

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Chase Zieman

Chief Data Officer

“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.”

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Rupak Patel

Operational Intelligence Manager

"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."

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How to Sync to Manually

Step 1: Export Data from ConvertKit

Begin by logging into your ConvertKit account. Navigate to the "Subscribers" tab and click on the option to export your subscriber data. ConvertKit will typically send you an email with a download link to a CSV file containing your subscriber data. Download this CSV file to your local machine.

Step 2: Prepare Data for BigQuery

Once you have the CSV file, open it using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data to ensure it is accurate and clean up any discrepancies such as duplicates or formatting issues. Save the cleaned data as a CSV file; ensure the file is encoded in UTF-8.

Step 3: Set Up Google Cloud Project

Go to the Google Cloud Console and create a new project if you haven't already done so. This will give you access to Google Cloud services, including BigQuery. Make sure that billing is enabled for your project to use BigQuery resources.

Step 4: Create a BigQuery Dataset

Within the Google Cloud Console, navigate to the BigQuery section. Here, create a new dataset by clicking on the "Create Dataset" button. Provide a name for your dataset and configure any necessary settings such as data location and expiration settings.

Step 5: Upload CSV File to Google Cloud Storage

Go to Google Cloud Storage in the Cloud Console and create a new bucket or use an existing one to upload your CSV file. Click on the "Upload Files" button and select your cleaned CSV file. This step is essential to facilitate the import of data into BigQuery.

Step 6: Load Data into BigQuery

In the BigQuery section of the Google Cloud Console, navigate to your dataset and click on "Create Table". Choose "Google Cloud Storage" as the source, and select the CSV file you uploaded earlier. Configure the schema by either auto-detecting or manually specifying the data types for each column. Click "Create Table" to load the data into BigQuery.

Step 7: Verify Data in BigQuery

Once the data loading process is complete, run a few SQL queries in the BigQuery console to verify that your data has been correctly imported. Check for data integrity and ensure that all necessary fields are present and correctly formatted. This will confirm that your data has been successfully transferred from ConvertKit to BigQuery.

By following these steps, you can efficiently move data from ConvertKit to BigQuery without relying on third-party connectors or integrations.