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Start by logging into your Klaviyo account. Navigate to the 'Analytics' section and select 'Export Data'. Choose the type of data you want to export (e.g., email lists, campaign metrics). Configure the export settings, such as date range and file format (CSV or JSON is recommended for ease of processing), and initiate the export. Download the exported file to your local system.
Open the exported file to ensure the data is complete and correctly formatted. Clean the data by removing any unnecessary columns or rows, and ensure that column headers are appropriately named as they will be used in Starburst Galaxy. Save the cleaned file in a location accessible for uploading.
Access your Starburst Galaxy account and ensure you have the necessary permissions to create catalogs and schemas. If not, consult with your Starburst Galaxy administrator. Navigate to the 'Data' section and create a new catalog and schema where the data will be stored.
In the Starburst Galaxy interface, use the SQL editor to create a table that matches the structure of your cleaned Klaviyo data. Write a `CREATE TABLE` statement defining the table's columns and data types to correspond with your data file. Execute the SQL statement to create the table within your designated schema.
Transfer the cleaned data file to a location accessible by Starburst Galaxy. This could involve uploading the file to a cloud storage service that's accessible from Starburst, or directly uploading it via Starburst's interface if supported. Ensure the file is accessible for the next step.
Use Starburst Galaxy's SQL capabilities to load data from the uploaded file into the created table. Execute a `COPY` or `INSERT INTO ... FROM` SQL command, specifying the file location and formatting options (such as delimiter and header presence if using CSV). Ensure the command maps file columns to the correct table columns.
Once the data is loaded, perform a verification check by running SQL queries to count records, check data integrity, and ensure no data is missing or misaligned. Compare these results with the original Klaviyo export to confirm successful data transfer. Address any discrepancies by reviewing and repeating relevant steps if necessary.
By following these steps, you'll successfully transfer data from Klaviyo to Starburst Galaxy 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?
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