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On this page
  • Overview
  • Architecture
  • Policy Enforcement
  • Registering Data Sources
  • Table Access
  • Mapping Users
  • Data Flow

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  1. Data and Integrations
  2. Databricks Spark
  3. Reference Guides

Databricks Spark Integration

PreviousReference GuidesNextDatabricks Spark Pre-Configuration Details

Last updated 3 months ago

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This page provides an overview of the Databricks integration. For installation instructions, see the .

Overview

Databricks is a plugin integration with Immuta. This integration allows you to protect access to tables and manage row-, column-, and cell-level controls without enabling table ACLs or credential passthrough. Policies are applied to the plan that Spark builds for a user's query and enforced live on-cluster.

Architecture

An Application Admin will configure Databricks with either the

  • on the Immuta App Settings page

  • where Immuta artifacts must be downloaded and staged to your Databricks clusters

In both configuration options, the Immuta init script adds the Immuta plugin in Databricks: the Immuta Security Manager, wrappers, and Immuta analysis hook plan rewrite. Once an administrator gives users Can Attach To entitlements on the cluster, they can query Immuta-registered data source directly in their Databricks notebooks.

Simplified Databricks configuration additional entitlements

The credentials used to do the Simplified Databricks configuration with automatic cluster policy push must have the following entitlement:

  • Allow cluster creation

This will give Immuta temporary permission to push the cluster policies to the configured Databricks workspace and overwrite any cluster policy templates previously applied to the workspace.

Policy Enforcement

Immuta best practices: Test user

Test the integration on an Immuta-enabled cluster with a user that is not a Databricks administrator.

Registering Data Sources

Table Access

The immuta Database

When a table is registered in Immuta as a data source, users can see that table in both the backing database and in the immuta database. This allows for an option to use the immuta database as a single database for all tables.

Fine-grained Access Control

Accessing Data

All access controls must go through SQL.

df = spark.sql("select * from immuta.table")
import org.apache.spark.sql.SparkSession

val spark = SparkSession
  .builder()
  .appName("Spark SQL basic example")
  .config("spark.some.config.option", "some-value")
  .getOrCreate()
val sqlDF = spark.sql("SELECT * FROM immuta.table")
%sql
select * from immuta.table
library(SparkR)
df <- SparkR::sql("SELECT * from immuta.table")

Note: With R, you must load the SparkR library in a cell before accessing the data.

Mapping Users

Data Flow

  1. The Immuta init script adds the immuta plugin in Databricks: the Immuta SecurityManager, wrappers, and Immuta analysis hook plan rewrite.

  2. Data source metadata, tags, user metadata, and policy definitions are stored in Immuta's Metadata Database.

  3. During Spark Analysis, Spark calls down to the Metastore to get table metadata.

  4. Immuta intercepts the call to retrieve table metadata from the Metastore.

  5. Immuta modifies the Logical Plan to enforce policies that apply to that user.

  6. Immuta wraps the Physical Plan with specific Java classes to signal to the SecurityManager that it is a trusted node and is allowed to scan raw data.

  7. The Physical Plan is applied and filters out and transforms raw data coming back to the user.

  8. The user sees policy-enforced data.

You should register entire databases with Immuta and run jobs through the Python script provided during data source registration. Additionally, you should use a Databricks administrator account to register data sources with Immuta using the UI or API; however, you should not test Immuta policies using a Databricks administrator account, as they are able to bypass controls.

A Databricks administrator can control who has access to specific tables in Databricks through Immuta Subscription Policies or by . Data users will only see the immuta database with no tables until they are granted access to those tables as Immuta data sources.

After data users have subscribed to data sources, administrators can apply fine-grained access controls, such as restricting rows or masking columns with advanced anonymization techniques, to manage what the users can see in each table. More details on the types of data policies can be found on the , including an .

Note: Immuta recommends building rather than Local Policies, as they allow organizations to easily manage policies as a whole and capture system state in a more deterministic manner.

Usernames in Immuta must match usernames in Databricks. It is best practice is to use the same identity manager for Immuta that you use for Databricks (Immuta supports . however, for Immuta SaaS users, it’s easiest to just ensure usernames match between systems.

An Immuta Application Administrator and registers available cluster policies Immuta generates.

A Data Owner . A Data Owner, Data Governor, or Administrator or user in Immuta.

A Databricks user who is subscribed to the data source in Immuta directly in their notebook or workspace.

Databricks Installation Introduction
Simplified Databricks Configuration
Manual Databricks Configuration
Schema Monitoring
manually adding users to the data source
Global Policies
configures the Databricks integration
registers Databricks tables in Immuta as data sources
creates or changes a policy
queries the corresponding table
Data Policies page
overview of masking struct and array columns in Databricks
these identity manager protocols and providers