BRIEF OVERVIEW

In order to read data from Azure Data Lake Storage (ADLS) using Databricks, you can follow these steps:

1. Create an instance of the SparkSession in your Databricks notebook.
2. Configure the necessary connection parameters for accessing ADLS. This includes providing the storage
   account name and access key or configuring service principal authentication.
3. Use the SparkSession’s built-in methods such as `read` or `spark.read` to load data from ADLS into a DataFrame.
   You can specify various file formats like Parquet, CSV, JSON, etc., depending on your needs.
4. You can then perform any required transformations or analysis on the loaded DataFrame using Spark SQL,
   DataFrame APIs, or other libraries available in Databricks environment.

FAQ: How do I configure access to ADLS in Databricks?

Answer:

To configure access to ADLS in Databricks, you need to provide either a storage account name and access key,
or use service principal authentication. For storage account name and access key method:

1. Create a secret scope with your Azure Key Vault details where you store your storage account name and
   access key securely. Follow this documentation for more information:
   https://docs.databricks.com/security/secrets/secret-scopes.html
2. Retrieve the secret values from the secret scope using Databricks’ Secret API. You can find more details here:
   https://docs.databricks.com/dev-tools/api/latest/secrets.html
3. Use these retrieved values to configure your ADLS connection parameters in your Databricks notebook.

FAQ: Can I read data from different file formats in ADLS?

Answer:

Yes, you can read data from various file formats available in ADLS such as Parquet, CSV, JSON, Avro,
and many others. Spark’s built-in methods like `read` or `spark.read` provide options to specify the format
while loading data into a DataFrame. For example:

// Reading Parquet files
df = spark.read.format("parquet").load("adl://{storage_account_name}/{container}/{file_path}")

// Reading CSV files with custom options
df = spark.read.format("csv")
             .option("header", "true")
             .option("inferSchema", "true")
             .load("adl://{storage_account_name}/{container}/{file_path}")
    

BOTTOM LINE

Databricks provides seamless integration with Azure Data Lake Storage (ADLS) for reading and processing large-scale
datasets. By following the steps mentioned above and utilizing Spark’s powerful capabilities, you can easily read
data from ADLS and perform various transformations and analysis in your Databricks environment.