How to load data from Klaviyo to BigQuery

Learn how to use Airbyte to synchronize your Klaviyo data into BigQuery within minutes.

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Bespoke pipelines are:
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Klaviyo connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted Klaviyo data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Klaviyo to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

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Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

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"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."

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How to Sync to Manually

Step 1: Export Data from Klaviyo

Begin by exporting the desired data from Klaviyo. Navigate to the specific data set you want to export, such as lists, campaigns, or metrics. Use Klaviyo's built-in export functionality to download the data in a CSV format. Ensure the exported data contains all necessary fields and is structured properly.

Set up your local environment to handle the data transformation. Ensure you have Python or any preferred scripting language available, along with necessary libraries like `pandas` for data manipulation. This step is crucial for cleaning and preparing the data before uploading it to BigQuery.

Import the CSV file into your script and use data manipulation tools to clean and transform the data as needed. This could involve renaming columns, changing data types, or filtering rows. Ensure the data structure aligns with your BigQuery schema to prevent issues during the upload process.

If not already installed, download and install the Google Cloud SDK on your local machine. This will allow you to interact with Google Cloud services, including BigQuery, from your command line. Authenticate your account with the `gcloud auth login` command to gain access.

In the Google Cloud Console, create a new dataset and table within BigQuery to store your Klaviyo data. Define the schema for your table, setting appropriate data types for each column. This step ensures that BigQuery knows how to interpret the incoming data.

First, upload the transformed CSV file to Google Cloud Storage. You can do this using the `gsutil cp` command provided by the Google Cloud SDK. Once the file is in Cloud Storage, use the BigQuery web UI or command line to load the data into your BigQuery table. Make sure to specify the correct Cloud Storage path and table schema during the import process.

After the data is uploaded to BigQuery, run queries to verify the integrity and correctness of the data. Check for any discrepancies or errors in the records. This step ensures that the data migration process was successful and the data is ready for analysis.

This guide should help you efficiently transfer data from Klaviyo to BigQuery using manual processes and native tools.