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Begin by logging into your ConvertKit account. Navigate to the subscribers’ page and select the option to export your data. ConvertKit typically allows you to export data in CSV format. Save the exported CSV file to your local machine.
Open the exported CSV file using a spreadsheet application like Microsoft Excel or Google Sheets. Clean and format the data to ensure consistency and accuracy. This may involve removing unnecessary columns, renaming headers to match Typesense's schema requirements, and ensuring data types are consistent.
Install Python on your local machine if it's not already installed. You will use Python scripts to process the data and communicate with the Typesense API. Ensure you have pip, the Python package manager, to install necessary libraries.
Visit the official Typesense website and follow the instructions to download and install a Typesense server on your local machine. Alternatively, you can use Docker to run a Typesense server container by executing the appropriate Docker run command as specified in the Typesense documentation.
Before importing data, you need to define a collection in Typesense. Use the Typesense API to create a new collection. Define the schema of the collection to match the structure of your cleaned CSV data. This can be done using a HTTP POST request with the collection schema defined in JSON format.
Create a Python script to read the CSV file and import the data into Typesense. Use Python libraries such as `csv` for reading the file and `requests` to interact with the Typesense API. The script should iterate over each row in the CSV file, format the data as required, and use the Typesense API to add documents to the created collection.
After running the Python script, verify that the data has been successfully imported into Typesense. Use the Typesense API to retrieve a few records from the collection and cross-check them with the original CSV data. Adjust your script or data as necessary if there are discrepancies.
By following these steps, you can move data from ConvertKit to Typesense manually 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:
