How to load data from Notion to BigQuery

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

Building your pipeline or Using Airbyte

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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 Notion 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 Notion 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 Notion 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.

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.

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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.

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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.

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.

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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

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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 Notion

Begin by exporting your data from Notion. Open the Notion page you want to export, click on the three-dot menu in the upper-right corner, and select "Export." Choose "Markdown & CSV" as the export format. This will download a ZIP file containing your data in CSV format, which is suitable for importing into BigQuery.

Step 2: Extract and Prepare CSV Files

Once the ZIP file is downloaded, extract its contents. You will find one or more CSV files corresponding to your Notion tables or databases. Open each CSV file in a spreadsheet editor like Excel or Google Sheets to ensure the data is structured correctly and clean up any anomalies or formatting issues.

Step 3: Set Up a Google Cloud Account

If you haven’t already, set up a Google Cloud account. Go to the Google Cloud Console (https://console.cloud.google.com/), and create a new project or select an existing project where you want to store your data. Ensure that BigQuery API is enabled for your project.

Step 4: Create a New BigQuery Dataset

In the Google Cloud Console, navigate to BigQuery. Click on your project name in the Explorer panel, and then click "Create Dataset." Provide a name for your dataset and configure the data location and other settings as needed. This dataset will serve as a container for your tables.

Step 5: Prepare the Data Schema

Before importing data into BigQuery, define the schema of your data. You need to specify the column names, data types (e.g., STRING, INTEGER, FLOAT), and any other necessary configurations. This can be done manually based on the contents of your CSV file or by using a tool like Google Sheets to generate schema definitions.

Step 6: Load CSV Data into BigQuery

Go back to BigQuery in the Google Cloud Console. Click on your dataset, then click "Create Table." Choose "Upload" as your source, and select the CSV file you wish to import. Configure the table schema you prepared earlier, set the file format to CSV, and complete any additional settings such as field delimiter and header row recognition. Click "Create Table" to load the data into BigQuery.

Step 7: Verify Data Integrity

After the data import is complete, it's crucial to verify that the data has been accurately transferred. Run a few SQL queries in BigQuery to check the contents of the new tables against your original data. Make sure that all records are present and that data types and values match your expectations. This step ensures that the data migration was successful and accurate.

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