How to load data from Customer.io to BigQuery

Learn how to use Airbyte to synchronize your Customer.io 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 Customer.io 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 Customer.io 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 Customer.io 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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Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

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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 secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

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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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Raman Singh

Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

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Chase Zieman

Chief Data Officer

“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

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Rupak Patel

Operational Intelligence Manager

"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 Customer.io

Begin by exporting your data from Customer.io. Log in to your Customer.io account, navigate to the Data Export section, and select the data you wish to export. Choose a suitable format like CSV or JSON and download the file to your local machine or a secure cloud storage service.

Once the data has been exported, ensure it is formatted correctly for BigQuery. Review the file for any inconsistencies or errors. BigQuery can handle both CSV and JSON formats, but ensure that CSV files are correctly delimited and JSON files adhere to a valid structure. Clean and transform the data as needed, ensuring all fields match the expected schema in BigQuery.

If not already done, set up a Google Cloud Platform project. Navigate to the GCP Console, create a new project, and enable billing. Ensure that BigQuery is enabled in your project by activating the BigQuery API from the API Library.

In the BigQuery section of the GCP Console, create a dataset to store your imported data. Datasets act as containers and help organize your data tables. Choose a dataset name and configure the data location (e.g., US or EU) according to your needs.

Before importing data into BigQuery, upload your exported data file to Google Cloud Storage. Use the GCP Console or command-line tools like `gsutil` to upload the file to a GCS bucket. Ensure the bucket is in the same location as your BigQuery dataset to avoid cross-location data transfer issues.

With the data stored in GCS, navigate to BigQuery in the GCP Console. Use the 'Create Table' option and select 'Google Cloud Storage' as the source. Specify the path to your data file in the GCS bucket, and configure the table schema, field data types, and other relevant options. Execute the load job to import the data into your BigQuery dataset.

After the data has been successfully loaded into BigQuery, verify the import by checking the table schema and contents. Use the BigQuery Console to run simple queries and ensure the data has been imported correctly and is accessible for analysis. Adjust any schema or data transformations as needed to align with your reporting requirements.
By following these steps, you can effectively move data from Customer.io to BigQuery without relying on third-party connectors or integrations.