BRIEF OVERVIEW

DBFS (Databricks File System) is a distributed file system that provides a scalable and reliable way to store large amounts of data in Databricks. It is an abstraction layer over cloud object storage like Amazon S3, Azure Blob Storage, or Google Cloud Storage.

To store files in DBFS, you can use the Databricks notebook interface or the Databricks CLI. Both methods provide easy ways to upload and manage your files within the DBFS environment.

FAQs

Q: How do I upload a file to DBFS using the notebook interface?

A: To upload a file using the notebook interface, follow these steps:

1. Select “Data” on the left sidebar of your workspace.
2. Click on “Upload File” button located at the top right corner of the Data page.
3. Select your desired file from your local machine and click “Open”. The file will be uploaded to DBFS under “/FileStore/tables/” directory by default.

Q: Can I programmatically store files in DBFS?

A: Yes, you can use various programming languages such as Python or Scala with appropriate libraries to interact with DBFS programmatically. For example, you can use PySpark’s `spark.read.csv()` method to read CSV files from any location accessible by Spark cluster into DataFrame directly without manual uploading via notebook interface.

Q: How do I access my stored files in DBFS?

A: You can access your stored files in DBFS by specifying the file path. For example, if you uploaded a file named “data.csv” to the default directory “/FileStore/tables/”, you can access it using the path “/FileStore/tables/data.csv”.

Q: Can I delete files from DBFS?

A: Yes, you can delete files from DBFS. To do this using the notebook interface:

1. Select “Data” on the left sidebar of your workspace.
2. Navigate to the location where your desired file is stored.
3. Hover over the file and click on the three-dot menu icon that appears.
4. Select “Delete” from the dropdown menu.

BOTTOM LINE

Storing files in DBFS provides a convenient way to manage and access data within Databricks. Whether through manual uploading via notebook interface or programmatically using appropriate libraries, storing files in DBFS allows for seamless integration with Spark-based workflows and analysis.