Apache Ranger : Fine-Grained Access Control

Explore Apache Ranger's components and architecture, including the policy server, audit server, plugins, and tag sync, to enable centralized security administration and fine-grained access control across Hadoop components.

install apache ranger 2.1.0 with prerequisites maven, git, gcc and mysql 5.6/5.7. build ranger-admin from source or run a shell script to automate setup, then start ranger-admin on port 6080.

Install Apache Ranger 2.4.0 using docker compose to set up ZooKeeper, Solr, and Postgres, then start Ranger admin and log in at localhost:6080 with admin and rangerR0cks!

Ranger admin portal and create Hive service, define policies, masking, and row-level filters, and leverage Atlas tag sync to enforce fine-grained access control.

Explore Apache Ranger Hive integration by installing the Ranger Hive plugin, loading sample data in Hive, and defining policies with data masking and row-level filters to authorize each user's queries.

Acquire a hive cluster and install the ranger hive plugin on EMR. Configure the ranger service, test the connection, and load sample data to define policies.

Load sample e-commerce data into Hive on an EMR cluster, create external tables for users, restaurants, and orders, and configure Apache Ranger policies for sales, delivery, and customer support.

Define fine-grained access policies in Apache Ranger for Hive, create sales, delivery executive, and customer support users, and verify access to tables and columns via Ranger policies and Hive queries.

Explore data masking by hashing delivery executive's access to user phone numbers, create an Apache Ranger masking policy using hash, verify hashed data in Hue, and preview row-level filtering.

Explore row-level filters in apache ranger to enforce city-based access. Create a policy for a Bangalore sales user to access only Bangalore restaurants, then verify with a Hive query.

Explore ranger hdfs integration, install the hdfs ranger plugin, load data from S3 using Hadoop command, and define policies to restrict Hive managed tables to customer support users.

Install the ranger hdfs plugin on a hive cluster via an emr step using installhdfsrangerplugin.sh, configure ranger with install.properties and the hdfs ranger ip address, then restart hdfs and hive.

Learn how to load sample data into HDFS, create Hive managed tables, and apply HDFS policies with Apache Ranger to restrict access for the delivery executive and customer support.

Define ranger policies to enforce hdfs permissions for hive managed tables, keeping dfs.permissions.enabled true, and grant read, write, and execute to delivery executive and customer support users.

Integrate Apache Ranger with Presto by provisioning an AWS Presto cluster, installing the Ranger plugin, loading sample data via the S3 connector, and defining and testing Ranger policies in Presto.

Install the Apache Ranger Presto plugin on an EMR cluster by running the installPrestoRangerPlugin.sh via scriptrunner.jar, configure the Ranger Presto service, restart Presto and Hue, and add the PrestoDev service.

Load sample data into presto by creating restaurants, users, and orders tables in hive, converting csv to orc, and verifying Apache Ranger policies by querying on presto.

Define fine-grained access policies in Apache Ranger and verify them with Presto using Hue, granting catalog read access and enforcing per-table and per-column permissions.

Learn to synchronize tags from apache atlas to apache ranger, enabling tag-based access and masking policies, with atlas 2.1.0 setup, ranger vm configuration, and hive validation.

Explore how Apache Atlas provides metadata management and governance, offering a central data catalog with lineages, relationships, classifications, tagging, schema, and audits across data stores.

Install Apache Atlas 2.1.0 with Maven 3.5+ and Java 1.8.0-151, download and untar Atlas, package with embedded HBase and Solr, start Atlas, and verify logs and admin login.

Register a Hive users table in Atlas via the REST API to create a Hive DB ranger and an external table on S3, with storage descriptor and qualified names.

Define tag-based policies in Apache Ranger that apply to new resources by tagging. Use TagSync from Apache Atlas to synchronize classifications like PII, enabling policy-based access control for Hive data.

Install Apache Ranger 2.1.0, set up the Hive Ranger plugin, create the Hive service and users table with Hue, and configure Atlas with external Kafka and ZooKeeper for notifications.

Install Ranger TagSync to turn Atlas classifications into Hive tag-based policies via Kafka, configure properties, start TagSync, create HiveDev_tag service, and verify PII tagging flows into Ranger.

Define tag-based access policies in Apache Ranger to protect PII in the users table using Hive Dev Service resource-based access, granting customer support full PII and delivery executive non-PII.

Define masking policies in Ranger to hash PII fields for customer support queries, using tag-based masking tab to create a policy named masking policy that masks email, phone, and gender.