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Start by logging into your ConvertKit account. Navigate to the specific data you want to export, such as subscribers or email sequences. Use ConvertKit's built-in export feature to download the data as a CSV file. This is typically found in the settings or a specific section's options menu.
Once you have the CSV file, open it using a spreadsheet application like Microsoft Excel or Google Sheets. Clean and format the data as necessary, ensuring that it’s organized in a way that aligns with your intended use in Google Pub/Sub. This includes making sure the headers and data types match the requirements of your publishing logic.
Install and configure the Google Cloud SDK on your local machine if you haven't already. This will allow you to interact with Google Cloud services from your command line. You can download it from the Google Cloud website. After installation, authenticate your account using `gcloud auth login` and set your default project with `gcloud config set project [PROJECT_ID]`.
Navigate to the Google Cloud Console and access the Pub/Sub service. Create a new topic where you will publish your data. This can be done via the console by clicking "Create a topic" and specifying a unique name for the topic. Alternatively, you can create a topic using the command line: `gcloud pubsub topics create [TOPIC_NAME]`.
Write a script in your preferred programming language (e.g., Python, Node.js) to read the CSV file and publish each row to the Pub/Sub topic. Make use of Google Cloud’s client libraries, which simplify interacting with Pub/Sub. For Python, you can use the `google-cloud-pubsub` library. Your script should authenticate using the service account credentials and iterate over the CSV rows, publishing them as messages to the topic.
Create a service account in the Google Cloud Console with Pub/Sub Publisher role permissions. Download the JSON key file for this service account. Ensure your script uses this key for authentication by setting the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to the path of your JSON key file.
Execute your script to start publishing data to Pub/Sub. Monitor the process by checking the Google Cloud Console's Pub/Sub section to ensure messages are being published successfully. You can also set up a subscription to the topic to pull and verify the messages being received. Adjust your script and data handling logic as necessary to handle any errors or data discrepancies.
Following these steps will enable you to manually transfer data from ConvertKit 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.
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:
