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Start by logging into your Klaviyo account. Navigate to the "Lists & Segments" or "Profiles" section, depending on the data you want to export. Use the export function to download the data in CSV format. Ensure you select all necessary fields that you will need in Typesense.
Open the exported CSV file using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data to ensure all necessary fields are present and correctly formatted. Remove any unnecessary columns or data that will not be used in Typesense to streamline the import process.
Typesense requires data in JSON format. Convert your cleaned CSV data to JSON. You can use a script in Python or a tool like OpenRefine to automate this process. Ensure the structure of your JSON matches the schema expected by your Typesense collection.
If you haven't already, set up a Typesense server. This can be done locally or on a cloud server. Download and install the Typesense binary from the official Typesense website, and start the server by running the `typesense-server` command. Ensure the server is running and accessible.
With your Typesense server running, create a new collection that will store your Klaviyo data. Define the schema for this collection using the Typesense API. The schema should include all fields from your JSON data, specifying field types and any other necessary parameters like sorting or faceting.
Use the Typesense API or a command-line tool like `curl` to import your JSON data into the newly created collection. Make POST requests to the Typesense endpoint, ensuring your data is correctly indexed. Handle any errors by checking the response from the API and adjusting your data or schema as needed.
Once the import process is complete, verify that the data has been correctly imported into Typesense. Use the Typesense API to perform search queries and check if the data is indexed and searchable as expected. Make any necessary adjustments based on this verification to ensure data integrity.
Following these steps will allow you to manually transfer data from Klaviyo to Typesense without the need for 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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