How to load data from GitHub to Databricks Lakehouse

Learn how to use Airbyte to synchronize your GitHub data into Databricks Lakehouse within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a GitHub connector in Airbyte

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

Set up Databricks Lakehouse for your extracted GitHub 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 GitHub to Databricks Lakehouse 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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How to Sync to Manually

Step 1: Access GitHub Repository

Begin by accessing your desired GitHub repository containing the data. Ensure you have the appropriate permissions to clone or download the repository. You can use GitHub's web interface to clone or download the repository locally to your machine.

Use the Git command-line interface to clone the repository to your local system. Execute the following command in your terminal:
```
git clone https://github.com/username/repository.git
```
Replace `username` and `repository` with the appropriate values for your GitHub repository.

Navigate to the cloned repository directory on your local machine. Locate the data files you need to move to Databricks. These files can be in formats like CSV, JSON, or Parquet. Ensure they are organized in a way that makes it easy to identify and access them.

Log in to your Databricks account. Create a new cluster or use an existing cluster if available. Ensure the cluster is running and you have the necessary permissions to create notebooks and upload data.

Use the Databricks UI to upload your data files from the local system to the Databricks File System. Navigate to the "Data" section, select "DBFS", and use the "Upload" button to transfer your files. This step makes your data accessible for further processing within Databricks.

In a Databricks notebook, write a script to mount the DBFS location to the lakehouse. Use the following Scala or Python code:
```python
dbutils.fs.mount(
source = "dbfs:/path/to/your/data",
mount_point = "/mnt/lakehouse",
extra_configs = {"":""}
)
```
Ensure you replace `"/path/to/your/data"` with the path where your data files are stored in DBFS, and `"/mnt/lakehouse"` with your preferred mount point.

Once mounted, verify the data's accessibility by listing the files in the mounted directory. Use the following command in a notebook cell:
```python
display(dbutils.fs.ls("/mnt/lakehouse"))
```
This step confirms that your data is correctly moved and accessible within the Databricks Lakehouse environment, ready for analysis or further processing.

By following these steps, you can manually move data from GitHub to a Databricks Lakehouse without relying on third-party connectors or integrations.