Features of Cloud Files

1. Cloud Object Storage Databricks provides seamless integration with major cloud storage services:
Amazon S3: Allows you to access and manage files stored in Amazon Web Services' Simple Storage Service.
Azure Data Lake Storage (ADLS) Gen2: Enables access to Microsoft's Azure cloud storage.
Google Cloud Storage: Provides access to Google's cloud storage services.
Azure Blob Storage: Another Azure cloud storage service that stores large amounts of unstructured data.

2. Unified Access
Databricks enables you to read and write data from cloud storage in a consistent manner using Apache Spark, SQL, and Databricks SQL.
Reading Data:
df = spark.read.format("csv").option("header", "true").load("s3://bucket-name/path/to/file.csv")
df.write.format("parquet").save("s3://bucket-name/path/to/output-folder/")
3. Auto Loader
Auto Loader automatically processes new data files as they arrive in cloud storage. It supports various formats like JSON, CSV, and Parquet.

Example:

df = spark.readStream.format("cloudFiles") .option("cloudFiles.format", "json") .load("s3://bucket-name/path/to/streaming/source/")
4. Databricks File System (DBFS) DBFS is a distributed file system in Databricks that lets you interact with cloud storage as if it were a local file system.

DBFS Commands:
dbutils.fs.ls("/mnt/path/to/directory/")
dbutils.fs.cp("dbfs:/source/path", "dbfs:/destination/path") 5. Unity Catalog
Unity Catalog provides a unified governance solution for managing data and metadata across different cloud storage services, improving data governance and compliance.

Features:
Centralized metadata management
Fine-grained access controls
Data lineage tracking

Example Workflow:
Mount Cloud Storage: Mount your cloud storage to Databricks using DBFS.
dbutils.fs.mount( source = "s3a://your-bucket", mount_point = "/mnt/your-mount-point", extra_configs = {"fs.s3a.access.key": "", "fs.s3a.secret.key": ""} )

Read Data: Read data from the mounted storage.

df = spark.read.format("csv").option("header", "true").load("/mnt/your-mount-point/path/to/file.csv")
Write Data: Write processed data back to the cloud storage.
df.write.format("delta").save("/mnt/your-mount-point/path/to/output-folder/")
By leveraging these capabilities, Databricks Cloud Files provide a robust and scalable way to manage and process data stored in the cloud.