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To begin, you need to export the data from Klaviyo. Log in to your Klaviyo account and navigate to the data you wish to export. Use Klaviyo's built-in export functionality to download the data in a CSV format. Ensure that the export covers all necessary fields for your analysis or storage needs.
Once exported, save the CSV files to your local machine. Choose a directory that is easily accessible for uploading to AWS. Verify the integrity of the files to ensure that all data has been exported correctly and is readable.
Log into your AWS Management Console and navigate to S3. Create a new S3 bucket or choose an existing one where you want to store the Klaviyo data. Ensure the bucket has appropriate permissions for uploading and accessing the data, using access policies if necessary.
Use the S3 console, AWS CLI, or SDKs to upload your CSV files from your local machine to the S3 bucket. If using AWS CLI, the command will look something like `aws s3 cp /local/path/to/file.csv s3://your-bucket-name/`, ensuring the file lands in the correct bucket and path.
In the AWS Management Console, go to AWS Glue and create a new crawler. Configure the crawler to point to the S3 bucket where your CSV data is stored. Define the IAM role with the necessary permissions for Glue to access your S3 data. Run the crawler to catalog the data in the Glue Data Catalog.
Once the data is cataloged, create a new Glue ETL job. In the Glue Console, select the source from the Glue Data Catalog that was created by the crawler. Define your data transformations if needed, and set the target as another S3 bucket or a different path in the same bucket. Choose the job script type (Python or Scala) and define any necessary ETL transformations.
Execute the Glue ETL job. Monitor the job execution in the Glue Console to ensure it completes successfully. Check the target S3 location to confirm that the transformed data is correctly written. Review any logs or errors in CloudWatch if the job does not execute as expected.
By following these steps, you can effectively move data from Klaviyo to Amazon S3 using AWS Glue 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.
Klavivo is a communications platform aimed at helping businesses grow through email and marketing automation. Klavivo does the granular work, from personalized newsletters and thank you’s to automated emails reminding visitors of abandoned carts and order follow-ups—so businesses don’t have to spend time on the little details. An inexpensive solution for businesses to customize email marketings campaigns, it integrates with a customer’s data sources at scale and allows brands to measure their results.
Klaviyo's API provides access to a wide range of data related to email marketing and e-commerce. The following are the categories of data that can be accessed through Klaviyo's API:
1. Profiles: This includes information about individual subscribers, such as their email address, name, location, and other demographic data.
2. Lists: This includes information about the different email lists that are managed within Klaviyo, such as the number of subscribers, the date they were added, and their engagement metrics.
3. Campaigns: This includes information about the different email campaigns that have been sent, such as the subject line, the content, and the performance metrics.
4. Metrics: This includes data related to the performance of email campaigns, such as open rates, click-through rates, and conversion rates.
5. Events: This includes data related to specific actions taken by subscribers, such as making a purchase, abandoning a cart, or signing up for a newsletter.
6. Products: This includes information about the products that are sold through an e-commerce store, such as their name, price, and availability.
7. Orders: This includes information about the orders that have been placed by customers, such as the order number, the date, and the total amount.
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:
