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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.
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.
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.
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.
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.
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.
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.
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?
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