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Hadoop Cluster Introduction

Audience: Data Owners and System Administrators

Content Summary: This guide augments the documentation on Spark, focusing on how and when you should use the Immuta Spark integration on your cluster.

Why Use Immuta On Your Cluster

When you create Hive or Impala tables from your data in HDFS, it may require policies restricting who can see specific rows and columns. This becomes complex on a Hadoop cluster because not only do you need to protect the Hive and/or Impala tables, but you also need to protect the data that back those tables.

For example, when you run SparkSQL, although it does reference Hive or Impala tables, it does not actually read any data from them. For performance reasons it reads the data directly from HDFS. This means that any protections you set on those Hive or Impala tables through Sentry or Ranger will not be applied to the raw file reads in SparkSQL. And in fact, those files need to be completely open to anyone running SparkSQL jobs.

Immuta enforces policy controls not only on the Hive and Impala tables, but also the backing files in HDFS.

Workflow

Should you want to enforce row and column level controls on data in HDFS, you must associate some structure to that data. This is done by creating tables in Hive or Impala from that data in HDFS. Once those tables are created, you can then expose them as data sources in Immuta like you normally would any other database.

The difference, though, is that Immuta will not only enforce the controls through the Immuta Query Engine, but will also dynamically lock down the backing files in HDFS. That means if anyone tries to read those files, they will be denied access. In order to read these files, users can use SparkSQL and the ImmutaSparkSession (Spark 2.4).

Tip: The user principal used to expose the data from Impala/HIVE/HDFS will not be impacted by Immuta security on the underlying files; it will fall back to the underlying permissions (such as ACLs).

Immuta Spark Session

The ImmutaSession class (Spark 2.4) is a subclass of SparkSession. Users can access subscribed data sources within their Spark jobs by using SparkSQL. Immuta enforces SparkSQL controls on data storage technologies that support batch processing workloads. Standard Spark libraries access data from metastore-backed data sources (like Hive and Impala) to retrieve the data from the underlying files stored in HDFS, while Immuta dynamically unlocks the files in HDFS and enforces row-level and column-level controls within the Spark job.

General Spark Access

Should you not care about row and column level controls, but still want to restrict access to files, you can do this with Immuta HDFS data sources. You can expose the HDFS directories in Immuta as data sources and enforce file-level controls based on directory structure or extra attributes on those files. In this case, HDFS reads work as usual and data is read with the Immuta policies enforced.

Policy Fallback

It is possible to also set ACL (or Ranger/Sentry) controls on tables and HDFS files as well. If an Immuta policy is set on that data, it will be enforced first, but if not, it will fall back to the ACL/Sentry/Ranger controls on that data. You can in fact exclude users (like admins) from Immuta policies should you desire to do so.

Please refer to our Installation Guide for details on combined installs with Immuta and Sentry. There are requirements on what sequence you install both.

Securing Hive and Impala without Sentry

Although Cloudera recommends using the Sentry service to secure access to Hive and Impala, CDH cluster administrators can lock down this access without running the Sentry service. See the Security without Sentry Guide for details on this alternative to using Sentry.

Data Sharing

It is recommended that you provide write scratch space to your users that is private to them, avoiding write to public locations in HDFS. This avoids the issue of users inadvertently sharing data or data outputs from their jobs with other users. Once that data is in their scratch space, users with CREATE_DATA_SOURCE permission can expose that data, either by exposing a Hive or Impala table created from it (if row/column controls are needed) or by exposing the raw HDFS files as an Immuta data source.

You may want to only allow privileged users have CREATE_DATA_SOURCE permission so the appropriate policies can be applied before the data is exposed.