Writing to Projects

Project workspaces

Deprecation notice

Support for this feature has been deprecated. See the Deprecations page for EOL dates.

With equalization enabled, project users can create project workspaces for Databricks where users can view and write data.

Using Immuta projects and project equalization, Databricks Spark project workspaces are a space where every project member has the same level of access to data. This equalized access allows collaboration without worries about data leaks. Not only can project members collaborate on data, but they can also write protected data to the project.

Users will only be able to access the directory and database created for the workspace when acting under the project. The Immuta Spark SQL Session will apply policies to the data, so any data written to the workspace will already be compliant with the restrictions of the equalized project, where all members see data at the same level of access. When users are ready to write data to the project, they should use the SparkSQL session to copy data into the workspace.

Databricks project workspace workflow

1. An Immuta user with the CREATE_PROJECT permission creates a new project with Databricks data sources.
2. The Immuta project owner enables project equalization which balances every project members’ access to the data to be the same.
3. The Immuta project owner creates a Databricks project workspace which automatically generates a subfolder in the root path specified by the application admin and remote database associated with the project.
4. The Immuta project members query equalized data within the context of the project, collaborate, and write data, all within Databricks.

Root directory details

• Immuta only supports a single root location, so all projects will write to a subdirectory under this single root location.
• If an administrator changes the default directory, the Immuta user must have full access to that directory. Once any workspace is created, this directory can no longer be modified.
• Administrators can place a configuration value in the cluster configuration (core-site.xml) to mark that cluster as unavailable for use as a workspace.

Read data

When acting in the workspace project, users can read data using calls like spark.read.parquet("immuta:///some/path/to/a/workspace").

Supported cloud providers

Microsoft Azure

Immuta currently supports the abfss schema for Azure General Purpose V2 Storage Accounts. This includes support for Azure Data Lake Gen 2. When configuring Immuta workspaces for Databricks on Azure, the Azure Databricks workspace ID must be provided. More information about how to determine the workspace ID for your workspace can be found in the Databricks documentation. It is also important that the additional configuration file is included on any clusters that wish to use Immuta workspaces with credentials for the container in Azure Storage that contains Immuta workspaces.

Google Cloud Platform

Immuta currently supports the gs schema for Google Cloud Platform. The primary difference between Databricks on Google Cloud Platform and Databricks on AWS or Azure is that it is deployed to Google Kubernetes Engine. Databricks handles automatically provisioning and auto scaling drivers and executors to pods on Google Kubernetes Engine, so Google Cloud Platform admin users can view and monitor the Google Kubernetes resources in the Google Cloud Platform.

Caveats and limitations

• Stage Immuta installation artifacts in Google Storage, not DBFS: The DBFS FUSE mount is unavailable, and the IMMUTA_SPARK_DATABRICKS_DBFS_MOUNT_ENABLED property cannot be set to true to expose the DBFS FUSE mount.
• Stage the Immuta init script in Google Storage: Init scripts in DBFS are not supported.
• Stage third-party libraries in DBFS: Installing libraries from Google Storage is not supported.
• Install third-party libraries as cluster-scoped: Notebook-scoped libraries have limited support. See the Databricks trusted libraries section for more details.
• Maven library installation is only supported in Databricks Runtime 8.1+.
• /databricks/spark/conf/spark-env.sh is mounted as read-only:

  • Set sensitive Immuta configuration values directly in immuta_conf.xml: Do not use environment variables to set sensitive Immuta properties. Immuta is unable to edit the spark-env.sh file because it is read-only; therefore, remove environment variables and keep them from being visible to end users.
  • Use /immuta-scratch directly: The IMMUTA_LOCAL_SCRATCH_DIR property is unavailable.
• Allow the Kubernetes resource to spin down before submitting another job: Job clusters with init scripts fail on subsequent runs.
• The DBFS CLI is unavailable: Other non-DBFS Databricks CLI functions will still work as expected.

Supported metastore providers for Databricks

To write data to a table in Databricks through an Immuta workspace, use one of the following supported provider types for your table format:

• avro
• csv
• delta
• orc
• parquet