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Begin by logging into your Klaviyo account. Navigate to the "Analytics" tab and select "Reports." Choose the data set you wish to export, such as "Campaign Results," "List Growth," or "Customer Profiles." Export the data as a CSV file by clicking on the "Export" button and selecting "CSV" as the file format. Save this file to your local system.
Open the exported CSV file using a spreadsheet application like Excel or Google Sheets. Review and clean the data to ensure that there are no inconsistencies or errors. Ensure that the formatting aligns with the data structure required by Convex. This might involve renaming columns, changing data types, or removing unnecessary fields.
Log into your Convex account and access the database management interface. Depending on your setup, this might be a web-based interface or a command-line tool. Ensure that you have the necessary permissions to import data into the desired data tables within Convex.
If a table does not already exist to accommodate your imported data, create a new one. Define the schema of the table to match the structure of your prepared CSV file. Specify the correct data types for each column, such as VARCHAR for text, INT for integers, and DATE for date fields, ensuring compatibility with the CSV data.
Use a script or tool to convert the cleaned CSV data into SQL INSERT statements. This can be done using a scripting language like Python or a spreadsheet formula. Each row in your CSV file should correspond to an INSERT statement targeting the appropriate table in Convex, ensuring that the values match the column order in the table schema.
Execute the generated SQL INSERT statements within the Convex database interface. You can accomplish this by pasting the statements into the SQL query editor or using a batch script to execute them sequentially. Monitor the process for any errors and ensure that all data is imported correctly.
After the data import is complete, run queries in Convex to verify that the data has been transferred accurately. Compare a sample of records between Klaviyo and Convex to ensure consistency. Check for any discrepancies or missing entries and address any issues by revisiting the previous steps as needed.
By following these steps, you can manually transfer data from Klaviyo to Convex 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:
