BRIEF OVERVIEW:

Azure Data Lake Storage (ADLS) Gen2 is a highly scalable and secure data lake solution provided by Microsoft. Azure Databricks, on the other hand, is an Apache Spark-based analytics platform that can be used for big data processing and machine learning workloads. By mounting ADLS Gen2 in Azure Databricks, you can easily access and process your data stored in the data lake.

To mount ADLS Gen2 in Azure Databricks, you need to perform the following steps:

1. Create an Azure Blob storage account with hierarchical namespace enabled.
2. Create a container within the blob storage account.
3. Generate a Shared Access Signature (SAS) token for accessing the container.
4. In your Azure Databricks workspace, create a new notebook or open an existing one.
5. Within the notebook, run the following code snippet to mount ADLS Gen2:

%python
storage_account_name = ""
container_name = ""
sas_key = ""
mount_point = "/mnt/" # Choose any desired folder name

dbutils.fs.mount(
    source=f"wasbs://{container_name}@{storage_account_name}.dfs.core.windows.net",
    mount_point=mount_point,
    extra_configs={
        f"fs.azure.sas.{container_name}.{storage_account_name}.dfs.core.windows.net": sas_key
    }
)

6. After running this code snippet successfully, you will have mounted your ADLS Gen2 container in Azure Databricks. You can now access the data within the container using the mount point specified.

FAQs:

Q: Can I mount multiple ADLS Gen2 containers in Azure Databricks?

A: Yes, you can mount multiple ADLS Gen2 containers by executing the code snippet mentioned above for each container separately. Simply provide a different mount point for each container.

Q: How do I unmount an ADLS Gen2 container from Azure Databricks?

A: To unmount an ADLS Gen2 container, execute the following code snippet within your notebook:

%python
dbutils.fs.unmount("/mnt/")

BOTTOM LINE:

Mounting ADLS Gen2 in Azure Databricks allows you to seamlessly work with your data stored in a highly scalable and secure data lake environment. By following the steps provided above, you can easily set up this integration and start leveraging the power of Apache Spark for your big data processing needs.