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Begin by exporting your data from ConvertKit. Log into your ConvertKit account, navigate to the Subscribers page, and use the Export function to download your subscribers’ data in a CSV format. Ensure that you have all the necessary fields needed for your Firestore database.
With your CSV file ready, open it in a spreadsheet editor like Google Sheets or Microsoft Excel. Review the data to ensure accuracy and consistency. Make any necessary adjustments to match the structure and schema you plan to use in Firestore.
If you haven't already, set up a Google Cloud Platform project and enable Firestore. Go to the Firebase console, create a new project, and navigate to the Firestore database section. Choose the appropriate database mode (production or test) and create a new Firestore database.
To interact with Firestore programmatically, install the Firebase CLI. Open your terminal and run the following command to install it via npm:
```bash
npm install -g firebase-tools
```
After installation, authenticate the CLI with your Google account by running:
```bash
firebase login
```
Since Firestore requires JSON format for data imports, convert your CSV data into JSON. You can write a simple script in Python or Node.js to accomplish this. The script should read the CSV file and output a JSON file structured according to your Firestore schema.
Create a script to upload your JSON data to Firestore. This can be done using the Firebase Admin SDK. Here is an example in Node.js:
```javascript
const admin = require('firebase-admin');
const serviceAccount = require('path-to-your-serviceAccountKey.json');
admin.initializeApp({
credential: admin.credential.cert(serviceAccount)
});
const firestore = admin.firestore();
const data = require('path-to-your-json-file.json');
data.forEach(async (item) => {
await firestore.collection('your-collection-name').add(item);
});
```
Replace `path-to-your-serviceAccountKey.json` and `path-to-your-json-file.json` with the actual paths to your files.
After running your import script, verify that the data has been correctly imported into Firestore. Use the Firebase console to check your Firestore database. Ensure that all fields are correctly populated and that the data matches what was in your CSV file.
By following these steps, you can effectively move data from ConvertKit to Google Firestore 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:





