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To export data from Klaviyo, first, you need to access their API. Obtain your Klaviyo API key from the Klaviyo account settings. This key will allow you to authenticate and make requests to the Klaviyo API.
Determine which data you need to export, such as lists, segments, or campaign reports. Refer to the Klaviyo API documentation to understand the endpoints and data structures. This will help you construct the correct API requests to retrieve the necessary data.
Develop a script using a programming language like Python, Node.js, or Ruby to make HTTP requests to the Klaviyo API. Use the API key to authenticate your requests. The script should iterate through the necessary endpoints and retrieve data in JSON or CSV format as required.
Once the data is fetched, transform it into a format suitable for S3 storage. If the data is in JSON, consider converting it to CSV if needed, as CSV files are easily previewed and processed. Ensure that the data structure aligns with how you intend to query or use it later.
Log into your AWS Management Console and create an S3 bucket if you haven't already. Ensure your bucket name is unique and configure any necessary permissions, such as enabling public access if required (though it's generally recommended to keep your S3 bucket private and secure).
Use the AWS SDK for your chosen programming language to programmatically upload your transformed data to the S3 bucket. Ensure your AWS credentials (access key and secret key) are securely managed, and the script has the necessary permissions to write to the S3 bucket.
To make data transfers seamless, schedule the script to run at regular intervals using a task scheduler like cron on Linux or Task Scheduler on Windows. This automation ensures that your data is consistently updated in S3 without manual intervention.
By following these steps, you can effectively move data from Klaviyo to Amazon S3 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:
