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First, log into your Klaviyo account. Navigate to the 'Lists & Segments' or 'Campaigns' section, depending on what data you need. Use the 'Export List to CSV' option to download your data. Make sure you export all necessary fields and understand the structure of your data, as this will be crucial for the next steps.
Open the exported CSV file(s) in a spreadsheet application like Excel or use a text editor. Review the data for any inconsistencies, duplicates, or errors. Clean the data by removing unnecessary columns, correcting any discrepancies, and ensuring the data types are consistent. This will help maintain data integrity when importing into ClickHouse.
Set up ClickHouse on your server if it's not already running. Ensure that your server meets the necessary requirements for ClickHouse. Create a database and tables that match the structure of the data you exported from Klaviyo. This can be done using the ClickHouse client or a web UI like Tabix.
While ClickHouse supports direct CSV imports, you might need to adjust the format slightly depending on your setup. Ensure that the CSV delimiter matches the expected delimiter for ClickHouse (usually a comma or tab) and that the file encoding is compatible (typically UTF-8).
Use a secure method like SCP (Secure Copy Protocol) or SFTP (SSH File Transfer Protocol) to transfer your cleaned and formatted CSV files to the server where ClickHouse is running. Make sure the files are placed in an accessible directory for the ClickHouse client to access.
Access the ClickHouse client on your server. Use the `INSERT INTO` command along with the `FORMAT CSV` option to load your data into ClickHouse. For example:
```sql
INSERT INTO my_table FORMAT CSV
[CSV file path];
```
Replace `my_table` with your actual table name and `[CSV file path]` with the path to your CSV file. Ensure that the columns in your CSV match the table schema.
Once the data is loaded, run queries to verify that the data in ClickHouse matches your expectations. Check for any missing rows or columns and validate the data types. Perform any necessary maintenance, such as optimizing tables or updating indices, to ensure efficient queries on the imported data.
By following these steps, you can manually move data from Klaviyo to ClickHouse without relying on third-party tools. This approach provides flexibility and control over the data migration process, ensuring data is handled as per your specific requirements.
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:
