How to load data from Sendinblue to BigQuery

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

Try Airbyte Cloud Manual method (the hard way)

Building your pipeline or Using Airbyte

Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.

Building in-house pipelines

Bespoke pipelines are:

Inconsistent and inaccurate data
Laborious and expensive
Brittle and inflexible

Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.

After Airbyte

Airbyte connections are:

Reliable and accurate
Extensible and scalable for all your needs
Deployed and governed your way

All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Sendinblue 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 Sendinblue 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 Sendinblue 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.

Try Airbyte Cloud Manual method (the hard way)

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

Talk to our team

Setup Complexities simplified!

You don’t need to put hours into figuring out how to use Airbyte to achieve your Data Engineering goals.

Simple & Easy to use Interface

Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.

Guided Tour: Assisting you in building connections

Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.

Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes

Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.

Try Airbyte Cloud Manual method (the hard way)

What sets Airbyte Apart

Modern GenAI Workflows

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.

Move Large Volumes, Fast

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.

An Extensible Open-Source Standard

More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

Full Control & Security

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.

Fully Featured & Integrated

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.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Raman Singh

Tech Lead at Symend

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

Learn more

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.”

Learn more

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."

Learn more

How to Sync to Manually

Step 1: Extract Data from Sendinblue

First, you need to extract the data you require from Sendinblue. Log in to your Sendinblue account, navigate to the "Contacts" or "Campaign Reports" sections, or any other area containing the data you need. Use the export feature to download the data in CSV format. Make sure to select all necessary fields during the export process.

Step 2: Prepare the Data for BigQuery

Once you have the CSV file from Sendinblue, inspect it to ensure it contains all required data and is formatted correctly. Check for any data inconsistencies or errors. If needed, clean the data using a tool like Excel, Google Sheets, or a script in Python or R to ensure compatibility with BigQuery.

Step 3: Set Up a Google Cloud Project

If you haven't already, set up a Google Cloud Project. Go to the Google Cloud Console, create a new project, and enable the BigQuery API. This step is crucial as it provides the workspace and resources needed to store and manage your data in BigQuery.

Step 4: Create a BigQuery Dataset and Table

In the BigQuery section of the Google Cloud Console, create a new dataset where your data will be stored. Within this dataset, create a new table with a schema that matches the structure of your CSV file. Define the columns and data types based on the CSV file structure to ensure proper data alignment.

Step 5: Upload the CSV File to Google Cloud Storage

Before importing the data into BigQuery, upload the CSV file to Google Cloud Storage. In the Google Cloud Console, navigate to Google Cloud Storage, create a new bucket if necessary, and upload your CSV file. This step acts as a staging area for your data before loading it into BigQuery.

Step 6: Load Data from Google Cloud Storage to BigQuery

Go back to BigQuery, open the dataset you created earlier, and click on "Create Table". Choose "Google Cloud Storage" as the source, select your uploaded CSV file, and configure the load settings. Ensure the table schema is correctly mapped to the CSV columns. Adjust any settings related to data handling, such as skipping headers or handling null values.

Step 7: Verify Data Import and Run Queries

After the data has been loaded into BigQuery, verify the success of the import by running simple queries to check the data integrity and completeness. Use SQL queries in the BigQuery console to ensure that all records are present, and the data types are correctly applied. This step ensures that the data is ready for analysis or further processing.

By following these steps, you can efficiently move data from Sendinblue to BigQuery without relying on third-party connectors or integrations.