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Begin by logging into your ConvertKit account. Navigate to the Subscribers tab and select "Export" to download your subscriber data in CSV format. Ensure that all necessary fields and tags are included in this export to capture all relevant data for your AWS Data Lake.
Access your AWS Management Console and ensure that you have the necessary permissions to create and manage AWS services. Set up an Amazon S3 bucket where you will store the data. If it doesn't exist, create a new bucket in a region that best fits your data residency and compliance requirements.
Open the CSV file you exported from ConvertKit and ensure the data is clean and properly formatted. This might involve removing any special characters, correcting data types, or modifying headers to match your AWS Data Lake schema. Save the cleaned file as a CSV or JSON format as required.
Access the S3 bucket via the AWS Management Console. Use the "Upload" function to transfer your formatted CSV or JSON file from your local system to the S3 bucket. Ensure that the bucket policies and permissions allow for proper access and use of the data.
Navigate to AWS Glue in the AWS Management Console. Configure a new Glue Crawler to catalog the data stored in your S3 bucket. Define the data source, choose the appropriate IAM role with permissions to access the S3 bucket, and specify the target database in the Glue Data Catalog.
Use AWS Glue to create an ETL (Extract, Transform, Load) job. This job will transform the data as necessary and load it into your AWS Data Lake (Amazon S3, Athena, Redshift, etc.). Define the ETL script using Glue's scripting interface or visual job editor, specifying the source S3 location, transformation logic, and target location.
Once the data is loaded into your AWS Data Lake, validate the successful transfer by querying the data using Amazon Athena or another querying tool of your choice. Confirm that all expected data is present and properly formatted. Adjust the ETL process as needed based on the results of your validation queries.
By following these steps, you'll be able to move your data from ConvertKit to an AWS Data Lake 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:
