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Begin by logging into your ConvertKit account. Navigate to the subscribers section and export your subscriber data. ConvertKit typically allows you to export data in CSV format. Ensure you have the export file saved on your local machine.
Ensure MongoDB is installed on your local machine or server where you intend to import the data. Additionally, install MongoDB tools which include `mongoimport`, a command-line utility to import data into MongoDB. You can download these from the MongoDB official website.
Open your exported CSV file and clean it up if necessary. Ensure that headers are correctly labeled and that there are no corrupt or missing fields. The file should be in a consistent format that MongoDB can recognize and import.
Since MongoDB uses BSON (Binary JSON) format, convert your CSV file to JSON. You can write a simple script using Python or another scripting language to achieve this. For example, using Python's `pandas` library:
```python
import pandas as pd
csv_file = 'path_to_your_convertkit_export.csv'
json_file = 'output.json'
df = pd.read_csv(csv_file)
df.to_json(json_file, orient='records', lines=True)
```
Launch the MongoDB shell by running `mongo` in your terminal. Create a new database and collection where you will import your data. For example:
```shell
use convertkitData
db.createCollection("subscribers")
```
Use the `mongoimport` tool to import the JSON file into your MongoDB database:
```shell
mongoimport --db convertkitData --collection subscribers --file output.json --jsonArray
```
Ensure the `convertkitData` database and `subscribers` collection match those you created in the previous step.
After running the `mongoimport` command, verify that the data has been correctly imported into MongoDB. Use the MongoDB shell to query the database:
```shell
use convertkitData
db.subscribers.find().pretty()
```
Check to ensure all records are present and correctly formatted within the MongoDB collection.
By following these steps, you can manually move data from ConvertKit to MongoDB without relying on external integrations or connectors.
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:





