Datahub Hive Kerberos Authentication

Did you know that nearly 70% of enterprises face challenges when managing secure data ingestion from protected environments? With the increasing reliance on tools like DataHub for data observability, the need for secure, seamless metadata ingestion has never been greater. Particularly when dealing with Kerberos-secured Hive clusters, organizations often struggle to balance security, automation, and operational efficiency. 

For one of our clients, this was a critical issue. They needed to integrate Hive metadata into DataHub while maintaining the integrity of their Kerberos authentication process. Their solution had to meet several key requirements: automation, security, and smooth integration into existing workflows. But as with many such challenges, the solution wasn’t straightforward. 

In this blog, we’ll walk you through how we developed a custom authentication layer that integrated Kerberos authentication directly into DataHub’s Hive ingestion pipeline. Our goal was to create a solution that not only ensured robust security but also automated the process and enhanced operational efficiency. Let’s explore how we achieved this and what it means for your organization’s data integration needs. 

The Challenge: Secure Hive Metadata Ingestion 

Hive clusters secured with Kerberos authentication present a unique set of challenges. The data pipeline needed to retrieve metadata without compromising security or causing operational inefficiencies. Here’s what was at stake: 

1. Seamless Kerberos Authentication: Without a proper authentication mechanism, DataHub couldn’t access Hive metadata in a secure environment. 
2. Automation: The ingestion process needed to be fully automated, fitting naturally into existing workflows. 
3. Error Handling & Logging: Robust error management was crucial to avoid bottlenecks, especially with issues like expired Kerberos tickets. 

Our solution had to address all of these concerns and provide a seamless integration for DataHub’s Hive metadata ingestion process. 

The Solution: Custom Authentication Layer for DataHub Hive Ingestion 

To tackle these challenges, we developed a custom solution that integrates Kerberos kinit into the DataHub ingestion process. This approach dynamically identifies Hive sources requiring Kerberos authentication and ensures that each authentication step is completed before the metadata ingestion pipeline runs.  

Key Solution Highlights 

• Automated Pre-Ingestion Authentication: We added a secure pre-ingestion step to handle Kerberos authentication automatically. 
• Python-Based Authentication Script: A custom Python script runs the kinit command, securely managing credentials and ensuring that authentication is smooth. 
• Recipe-Driven Execution: The ingestion pipeline is modified to dynamically trigger Kerberos authentication based on the ingestion recipe configuration. 
• Robust Error Handling: We implemented comprehensive error logging and recovery mechanisms to manage failed authentication attempts. 

Solution Architecture: How It Works 

Here’s a high-level overview of the system architecture for the customized solution: 

Workflow Diagram:

The How It Works: A Step-By-Step Process 

Here’s how the solution functions in the context of the DataHub ingestion pipeline: 

1. Recipe Parsing: The ingestion script (run_ingest.sh) inspects the ingestion recipe to determine which Hive sources require Kerberos authentication. 
2. Authentication Trigger: If Kerberos authentication is needed, the script calls the hive_kerberos_auth.py Python script. 
3. Kerberos Authentication: The Python script securely retrieves credentials via a secret management system, executes kinit to authenticate with the Kerberos Key Distribution Center (KDC), and proceeds if authentication is successful. 
4. Metadata Ingestion: Once authenticated, the standard DataHub ingestion pipeline begins, cataloging the necessary metadata from Hive. 

Technical Implementation: Python-Based Kerberos Authentication 

1. Python-Based Kerberos Authentication Script 

The core of the solution lies in the Python-based authentication script (hive_kerberos_auth.py), which automates the Kerberos authentication process. 

Key Features: 

 Secure Credential Handling: The script integrates with a secret management system to securely fetch the credentials needed for Kerberos authentication. 

Subprocess Execution: Using Python’s subprocess module, the script executes the kinit command, ensuring the Kerberos authentication happens in a secure and isolated manner. 

Error Reporting: If authentication fails, the script logs the error with detailed information for troubleshooting.  

Code Snippet

import subprocess import logging
def authenticate_with_kerberos(username, password):
try:
# Execute kinit command with username and password
process = subprocess.run(
["kinit", username],
input=password.encode(),
capture_output=True,
check=True
)
logging.info("Kerberos authentication successful.")
except subprocess.CalledProcessError as e:
logging.error(f"Kerberos authentication failed:
{e.stderr.decode()}")
raise

2. Modified Ingestion Workflow 

To ensure that the custom authentication process was integrated into the DataHub pipeline, we updated the ingestion script (run_ingest.sh). Here’s how it works:  

Recipe-Driven Trigger 

if grep -q "kerberos_auth: true" recipe.yml; then
python3 hive_kerberos_auth.py
fi
#Proceed with ingestion datahub ingest -c recipe.yml

 The ingestion script checks the recipe.yml file to see if Kerberos authentication is required.

Dynamic Execution 

If Kerberos authentication is needed, the script invokes the hive_kerberos_auth.py script. 

Metadata Ingestion 

After successful authentication, the usual DataHub ingestion process begins, ingesting metadata from the Hive cluster. 

if grep -q "kerberos_auth: true" recipe.yml; then
python3 hive_kerberos_auth.py
fi
# Proceed with ingestion
datahub ingest -c recipe.yml

Error Handling and Logging 

Given the critical nature of Kerberos authentication, we implemented robust error handling and logging mechanisms to ensure reliability throughout the process. Here are the key features: 

Retry Logic: The system automatically retries authentication for transient errors, reducing the chances of failure. 

Detailed Logs: Errors are logged with detailed messages, providing insights into authentication issues, which helps with troubleshooting. 

Challenges and Trade-Offs 

While the solution was effective, it wasn’t without its challenges:  

• Integration Complexity: Integrating the Kerberos authentication layer into DataHub’s existing ingestion framework required careful planning and testing. 
• Credential Management: Storing and securely retrieving credentials in compliance with security policies was a critical concern. 
• Error Recovery: Handling expired Kerberos tickets or incorrect credentials added complexity to the error recovery process 

 Trade-Offs 

• Increased Maintenance Overhead: Adding a custom authentication introduced additional complexity, which requires ongoing maintenance. 
• External Dependencies: The reliance on Kerberos and kinit requires managing these external tools effectively, , which adds an extra layer of dependency. 

Results and Impact 

The custom solution successfully addressed the client’s requirements, delivering significant improvements in security, automation, and usability. 

Key Outcomes   

1. Security Compliance: We ensured seamless metadata ingestion in a Kerberos-secured Hive environment. 
2. Operational Efficiency: The automated Kerberos authentication process eliminated manual steps, streamlining the workflow. 
3. Scalability: This solution is reusable across multiple Kerberos-secured Hive clusters, making it scalable for large enterprise environments. 

Performance Metrics 

• Authentication Time: ~2 seconds per ingestion run. 
• Ingestion Throughput: Maintained performance with negligible overhead. 
• Error Rate: 100% authentication success rate during testing with valid credentials. 

Conclusion: Securing Metadata Ingestion Without Compromise 

When dealing with Kerberos-secured Hive clusters, the challenge of ensuring seamless and secure metadata ingestion can feel overwhelming. Often, security protocols complicate the process, making it difficult to maintain the integrity of the ingestion pipeline without adding unnecessary complexity or manual interventions. However, the custom Kerberos authentication solution can help to overcome these hurdles and create a reliable, automated workflow that integrates securely into DataHub. 

This solution not only preserves the security standards required for Kerberos authentication but also automates the entire process, eliminating the need for manual intervention. By incorporating Kerberos authentication as a pre-ingestion step, the metadata from Hive clusters could be ingested safely and efficiently, without disrupting the performance or scalability of the DataHub platform. The use of a Python script to manage the authentication, combined with robust error handling and logging, further can streamline the process and improve operational reliability. 

For organizations facing similar challenges in securely integrating Hive clusters with DataHub, this solution serves as a blueprint for overcoming authentication barriers without compromising on security or automation. With the right tools and approach, integrating secure metadata ingestion can be both efficient and seamless—giving your teams the confidence to work with data from even the most secure environments without sacrificing performance or reliability.  

If you're ready to streamline your secure metadata pipelines, this solution could be exactly what you need.