Apache Ranger for authorization in Hadoop - Mini Project: Build & Apply

Apache Ranger for Authorization

📖 Scenario: You are working in a company that uses Hadoop to store and process big data. To keep data safe, the company uses Apache Ranger to control who can access which data.Apache Ranger helps set rules so only the right people can see or change data.

🎯 Goal: You will create a simple setup to define user permissions using Apache Ranger concepts. You will create a list of users and their roles, set a permission level, check who can access data, and finally print the users who have access.

📋 What You'll Learn

Create a dictionary of users with their roles

Create a variable for minimum permission level required

Use a loop to find users with permission level equal or higher than the minimum

Print the list of users who have access

💡 Why This Matters

🌍 Real World

Apache Ranger is used in big data environments to control who can see or change data. This project shows a simple way to check user permissions.

💼 Career

Understanding authorization helps data engineers and security analysts protect sensitive data and comply with company policies.

1

Create a dictionary of users and their roles

Create a dictionary called user_roles with these exact entries: 'alice': 'admin', 'bob': 'user', 'carol': 'guest', 'dave': 'user', 'eve': 'admin'

Hadoop

# Create the dictionary user_roles with given users and roles
# Your code here

Hint

Use curly braces {} to create a dictionary. Each entry has a username as key and role as value.

2

Set the minimum permission level required

Create a variable called min_permission and set it to the string 'user' to represent the minimum role needed to access data.

Hadoop

user_roles = {'alice': 'admin', 'bob': 'user', 'carol': 'guest', 'dave': 'user', 'eve': 'admin'}
# Set min_permission to 'user'
# Your code here

Hint

Just assign the string 'user' to the variable min_permission.

3

Find users with permission level equal or higher than minimum

Create a list called allowed_users that contains usernames from user_roles whose role is either 'admin' or 'user'. Use a for loop with variables user and role to iterate over user_roles.items().

Hadoop

user_roles = {'alice': 'admin', 'bob': 'user', 'carol': 'guest', 'dave': 'user', 'eve': 'admin'}
min_permission = 'user'
# Create allowed_users list with users having role 'admin' or 'user'
# Your code here

Hint

Use a for loop to check each user's role. If role is 'admin' or 'user', add the username to allowed_users list.

4

Print the list of users who have access

Write a print statement to display the allowed_users list.

Hadoop

user_roles = {'alice': 'admin', 'bob': 'user', 'carol': 'guest', 'dave': 'user', 'eve': 'admin'}
min_permission = 'user'
allowed_users = []
for user, role in user_roles.items():
    if role in ['admin', 'user']:
        allowed_users.append(user)
# Print the allowed_users list
# Your code here

Hint

Use print(allowed_users) to show the list of users who have access.

Practice

1. What is the main purpose of Apache Ranger in a Hadoop ecosystem?

easy

A. To speed up data processing tasks

B. To store large amounts of data efficiently

C. To control who can access data and what actions they can perform

D. To backup data automatically

Solution

1. Step 1: Understand Apache Ranger's role

   Apache Ranger is designed to manage security by controlling access to data.

2. Step 2: Compare options with Ranger's function

   Only To control who can access data and what actions they can perform describes access control, which matches Ranger's purpose.

3. Final Answer:

   To control who can access data and what actions they can perform -> Option C

4. Quick Check:

   Authorization = Access control [OK]

Hint: Remember: Ranger = data access control tool [OK]

Common Mistakes:

• Confusing Ranger with data storage tools
• Thinking Ranger speeds up processing
• Assuming Ranger handles backups

2. Which of the following is the correct way to define a policy in Apache Ranger?

easy

A. Specify users/groups, resources, and allowed actions

B. Write SQL queries to filter data

C. Create MapReduce jobs for data processing

D. Configure Hadoop cluster nodes

Solution

1. Step 1: Identify what a Ranger policy includes

   A policy defines who (users/groups) can do what (actions) on which data (resources).

2. Step 2: Match options to policy components

   Only Specify users/groups, resources, and allowed actions correctly lists these elements.

3. Final Answer:

   Specify users/groups, resources, and allowed actions -> Option A

4. Quick Check:

   Policy = Users + Resources + Actions [OK]

Hint: Policies = users/groups + resources + permissions [OK]

Common Mistakes:

• Confusing policies with SQL queries
• Mixing policy with data processing tasks
• Thinking policies configure cluster hardware

3. Given the following Ranger policy snippet:
{"users": ["alice"], "resources": {"database": "sales"}, "permissions": ["select", "update"]}
What actions can user 'alice' perform on the 'sales' database?

medium

A. Only select data

B. Select and update data

C. Delete data

D. No actions allowed

Solution

1. Step 1: Read the policy details

   User 'alice' is allowed 'select' and 'update' permissions on the 'sales' database.

2. Step 2: Determine allowed actions

   Since both 'select' and 'update' are listed, alice can perform both actions.

3. Final Answer:

   Select and update data -> Option B

4. Quick Check:

   Permissions = select + update [OK]

Hint: Check permissions list carefully for allowed actions [OK]

Common Mistakes:

• Assuming delete permission without it listed
• Ignoring update permission
• Thinking no actions allowed if unsure

4. You created a Ranger policy but users report they cannot access the resource. Which of the following is the most likely error?

medium

A. The policy does not include the correct user or group

B. The Hadoop cluster is down

C. The data is corrupted

D. The policy has too many permissions

Solution

1. Step 1: Analyze why access is denied

   If users cannot access, often the policy lacks their user or group in its definition.

2. Step 2: Evaluate other options

   Cluster down or data corruption would cause broader issues, not just access denial. Too many permissions does not block access.

3. Final Answer:

   The policy does not include the correct user or group -> Option A

4. Quick Check:

   User/group missing = access denied [OK]

Hint: Check user/group inclusion in policy first [OK]

Common Mistakes:

• Blaming cluster or data issues prematurely
• Assuming too many permissions cause denial
• Not verifying policy user/group settings

5. You want to create a Ranger policy that allows group 'data_scientists' to read any table in the 'analytics' database but not modify data. Which permission set should you assign?

hard

A. no permissions

B. select and update permissions

C. all permissions including delete

D. select permission only

Solution

1. Step 1: Understand the requirement

   The group should only read data, which means 'select' permission is needed without any modify permissions.

2. Step 2: Match permissions to requirement

   Only 'select' permission allows reading without modification.

3. Final Answer:

   select permission only -> Option D

4. Quick Check:

   Read only = select permission [OK]

Hint: Read-only access = select permission only [OK]

Common Mistakes:

• Giving update permission when only read is needed
• Assigning all permissions unnecessarily
• Leaving permissions empty causing no access