How to load data from Notion to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Notion data into Databricks Lakehouse within minutes.
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
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
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.
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
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend
Chase Zieman
“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.”
Rupak Patel
"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."
How to Sync to Manually
Step 1: Export Data from Notion
Begin by exporting the data from Notion. Open your Notion workspace, navigate to the page or database you want to export, click on the three-dot menu in the upper-right corner, and select "Export." Choose "CSV" as the export format if you are exporting a database, as this format is easily manageable and compatible with most data processing tools.
Step 2: Download the Exported Files
After initiating the export process, Notion will generate a downloadable file. Download this file to your local system. If you've exported multiple pages or a large database, ensure all files are downloaded and accessible for the next steps.
Step 3: Organize and Inspect the Data
Once downloaded, organize your CSV files in a dedicated folder on your local machine. Open these files using a spreadsheet tool (like Excel) to inspect the data structure, ensuring that the data is clean and correctly formatted. Take note of any necessary transformations or cleaning needed to prepare the data for upload.
Step 4: Prepare the Data for Upload
If necessary, clean and transform your CSV data to match the schema requirements of your Databricks Lakehouse. This may involve renaming columns, changing data types, or removing invalid entries. Save the cleaned file in a CSV format, ensuring it is ready for the upload process.
Step 5: Set Up a Databricks Workspace
Access your Databricks account and ensure your workspace is set up for data import. If you haven't already, create a new cluster or use an existing one to handle data processing tasks. This setup is crucial for uploading and processing your Notion data within the Databricks environment.
Step 6: Upload CSV Files to Databricks File System (DBFS)
Use the Databricks web interface to upload your CSV files to the Databricks File System (DBFS). Navigate to the "Data" section, select "Add Data," and then choose "Upload File." Select your prepared CSV files from your local system and upload them to a directory within DBFS.
Step 7: Load Data into a Databricks Table
With your files on DBFS, use Databricks Notebooks to load the CSV data into a table within your Lakehouse. Open a new notebook and use PySpark or Scala to create a DataFrame from the CSV files. Example PySpark code:
```python
df = spark.read.csv("dbfs:/path/to/your/csvfile.csv", header=True, inferSchema=True)
df.write.format("delta").saveAsTable("notion_data")
```
This code reads the CSV file into a DataFrame and then writes it into a Delta table within your Databricks Lakehouse.
By following these steps, you can move your data from Notion to the Databricks Lakehouse without relying on third-party connectors or integrations, ensuring a seamless data transfer process.