BRIEF OVERVIEW:
Databricks is a powerful cloud-based platform that allows you to process and analyze large datasets using Apache Spark. While Databricks provides various ways to access data, reading local files can be a bit tricky due to its distributed nature.
To read a local file in Databricks, you need to first upload the file into the Databricks workspace or mount it as a DBFS (Databricks File System) path. Once the file is accessible within your workspace, you can use Spark APIs or SQL commands to read and manipulate the data.
FAQs:
Q: How do I upload a local file into Databricks?
A: To upload a local file into Databricks, follow these steps:
1. Go to your Databricks workspace.
2. Click on “Workspace” from the sidebar menu.
3. Navigate to the desired folder where you want to store your file.
4. Click on “Upload” button at the top-right corner of the screen.
5. Select your local file and click “Open”.
The uploaded file will now be available within your Databricks workspace.
Q: How do I mount a local directory as DBFS path?
A: To mount a local directory as DBFS path in databrick’s notebook follow these steps:
1. Create an Azure Blob Storage account if not already done.
2. In Azure portal search for storage accounts
Note:The above image is for illustrative purposes only, the actual Azure portal may look different.
3. Click on “Add” to create a new storage account.
4. Fill in the required information and click “Review + Create”.
5. Once created, go to your Databricks workspace and navigate to your notebook.
6. Run the following command within a cell:
“`
dbutils.fs.mount(
source=”wasbs://
mount_point=”/mnt/
extra_configs={
“
}
)
“`
Replace `
The local directory will now be mounted as DBFS path under `/mnt/
BOTTOM LINE:
Reading local files in Databricks requires either uploading them into the workspace or mounting them as DBFS paths. By following these steps and using Spark APIs or SQL commands, you can easily read and process data from local files within Databricks.