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To access data from Klaviyo, you'll need to set up API access. Go to your Klaviyo account, navigate to "Account" > "Settings" > "API Keys", and generate a new private API key. This key will be used to authenticate your API requests to Klaviyo.
Determine which data you want to move from Klaviyo. Klaviyo’s API allows access to various data types such as lists, segments, events, and profiles. Review the Klaviyo API documentation to understand the endpoints and data structures.
Create a script using a language like Python to make HTTP GET requests to Klaviyo's API endpoints. Use the `requests` library to authenticate using your API key and fetch the desired data. Ensure that your script handles pagination, as Klaviyo may return large datasets in pages.
Create a new project in Google Cloud Platform (GCP) or use an existing one. Enable the Pub/Sub API by navigating to the API & Services > Library and searching for "Pub/Sub". Click "Enable" to activate it for your project.
Within your Google Cloud Project, create a new Pub/Sub topic where you will publish the data. Navigate to Pub/Sub in the GCP console, click on "Create Topic", and give your topic a name. This topic will serve as the channel for your data.
Extend your script to publish the extracted data to Google Pub/Sub. Use the `google-cloud-pubsub` library in Python to authenticate with your GCP project and publish messages to your topic. Each piece of data extracted from Klaviyo should be converted into a message format suitable for Pub/Sub.
To ensure data is moved regularly, set up a scheduler to run your script at desired intervals. On a Linux server, you can use `cron` jobs, while on a Windows server, use Task Scheduler. This will automate the extraction and publishing process, ensuring your data is consistently and efficiently moved from Klaviyo to Google Pub/Sub.
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:
