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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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.
Step 2: Clone Repository Locally
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.
Step 3: Export Data Files
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.
Step 4: Prepare Databricks Environment
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.
Step 5: Upload Data to Databricks File System (DBFS)
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.
Step 6: Mount DBFS to Lakehouse
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.
Step 7: Verify and Access Data
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.