The choice between Apache Kafka vs. Confluent Kafka comes down to factors like cost, customization, and expertise. Apache Kafka is the top data platform for storing and streaming distributed events at scale, but it can be incredibly complex to manage internally — which is why Confluent offers a commercial version. With Confluent Kafka, enterprises can outsource the maintenance of their Kafka implementation if they lack the in-house resources to support it. The tradeoff, of course, is paying a premium to use free open source software, and having less control over your data infrastructure.   

Related Whitepaper: The Decision Maker’s Guide to Apache Kafka

In this blog, we will consider the benefits and drawbacks of deploying open source Kafka to help teams decide whether the convenience of Confluent’s Kafka-based platform is worth the expense.

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Apache Kafka vs. Confluent Kafka Overview

The main difference between Apache Kafka and Confluent Kafka is that Apache Kafka is free open source software and Confluent Kafka is an open core platform, meaning it is a commercial product derived from open source Kafka. 

Read more about community vs. commercial OSS >>

What Is Apache Kafka?

Apache Kafka is an open-source distributed streaming platform built to handle real-time data feeds. It allows you to publish, subscribe to, store, and process streams of records in a durable, fault-tolerant way—making it a powerful tool for applications that require high-performance data streaming. 

Key features:

• Ability to handle millions of messages per second with low latency
• Strong durability for long-term record storage
• Flexibility for a variety of use cases, from data pipelines to event sourcing

While Apache Kafka can be used in applications as a message broker, its primary strength lies in its functionality as a distributed stream processing platform. 

Read about use cases for Kafka stream processing >>

What Is Confluent Kafka? 

Confluent Kafka is virtually identical to open source Kafka with the addition of a few proprietary, enterprise-grade features. It simplifies operations with tools for data streaming management, making it an appealing option for businesses that want a turnkey solution without maintaining complex infrastructure.

Key features:

• Managed cloud services
• Advanced monitoring tools through the Confluent Control Center
• Automated scaling and integration tools

Unlike Apache Kafka, which is free, Confluent Kafka has a subscription-based pricing model. 

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Benefits and Challenges of Open Source Kafka 

Let’s start with some of the advantages of Apache Kafka:

Benefits 

1. Cost-Effective: Open source and free to use, eliminating upfront license fees. While it’s important to remember the soft costs that come with any open source software adoption (such as having personnel with the necessary skills and experience to support it), the technology itself costs nothing. 
    
2. Customizability: Data infrastructure can be tailored to unique needs, which is ideal for businesses desiring flexibility or that must adhere to specific compliance standards.
    
3. Data Sovereignty: Full control over your deployments, and the freedom to make critical decisions about where it is hosted (on-prem, cloud, or hybrid environment). 
    
4. No Vendor Lock-In: No obligation to use other Confluent products and no limitations on technologies you can integrate.
    
5. Faster Updates: Upgrades and new features come out in open source Kafka first, whereas Confluent Kafka customers sometimes have to wait days, weeks, or even months for Confluent to catch up.  

Challenges 

Deploying Kafka in-house comes with some obstacles, namely:

1. Operational Complexity: Requires in-house expertise to set up, monitor, and manage clusters effectively. Kafka’s release schedule (three per year, with security fixes for only one year per version) also demands regular upgrades to avoid disruptions to workflows.
    
2. Support: The open source community can sometimes provide answers, but they might not be applicable to your use case. Plus, with no official support team or SLAs for critical Kafka issues, there is greater risk of downtime and wasting dev resources on troubleshooting.  

In short, Apache Kafka is an excellent choice for those seeking flexibility and independence, but demands a skilled team to manage its intricacies.

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Get the Decision Maker’s Guide to Kafka

Leverage the power of Kafka at enterprise scale with this white paper that delivers in-depth guidance on how to successfully implement Kafka and optimize deployments. 

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Benefits and Challenges of Confluent Kafka 

Why do some teams choose Confluent Kafka? 

Benefits

1. Simplified Operations: Managed services offload the complexity associated with setting up and maintaining Apache Kafka clusters.
    
2. Enhanced Monitoring and Scaling: The Confluent Control Center provides powerful tools for monitoring, cluster management, and scaling based on traffic.
    
3. Less Technical Expertise Needed: Enterprise-level tools minimize the need for a deeply technical, in-house team. 

Challenges 

The drawbacks of Confluent Kafka include:

1. Higher, Less Predictable Costs: Confluent Kafka has usage-based pricing models on top of licensing fees, so monthly billing may vary, particularly if you do not control the in-flow of data and need to autoscale. Technical support and proprietary enterprise tools (which may or may not be utilized) are also included in the cost of a Confluent Kafka subscription. 
    
2. Less Control Over Data: Limited ownership over your infrastructure may pose challenges in meeting specific security and compliance needs.
    
3. Vendor Constraints: Dependency on Confluent may result in reduced flexibility and adaptability for custom requirements over time. Organizations often find themselves paying for features they do not, and may never, use. 

To sum it up, Confluent Kafka is a good fit for organizations that prioritize operational efficiency and don’t mind the associated expenses.

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Final Thoughts 

Apache Kafka offers unmatched customization, cost-efficiency, and independence, which is ideal for organizations with the technical expertise to manage their own data infrastructure. Meanwhile, Confluent Kafka simplifies processes and reduces maintenance burdens, providing a hassle-free approach, but at significantly greater cost.

As you weigh your Kafka options, don’t forget to factor in your team’s abilities. Kafka is very complex, and problems can escalate quickly when it is deployed at scale. If relying on the Kafka community for support makes you nervous, remember that there are open source support vendors like OpenLogic who can assist you and deepen the Kafka expertise on your bench.

In 2024, Perforce OpenLogic collaborated with the Open Source Initiative and the Eclipse Foundation to launch a global survey about the use of open source software (OSS) in organizations. We invited open source users to tell us about OSS adoption and culture at their organizations — what technologies they rely on, how they are investing budget and resources, and the challenges they encounter. As always, we received responses from folks all over the world, working in different industries, for companies large (more than 5,000 employees), small (under 20 employees), and everything in between. 

The results of that survey, along with expert analysis, are now available in the 2025 State of Open Source Report. In this blog, I’ll touch on some of the major takeaways, trends, and themes that emerged from this year’s dataset, and what they indicate about the future of open source.

Related Report: 2025 State of Open Source Report

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OSS Adoption Is Still Surging

96% of the respondents increased or maintained their use of OSS, with 26% of them reporting a significant increase in usage. Cloud-native and container technologies are receiving the most investment, followed closely by open source databases and data technologies, and programming languages (which are particularly a priority for smaller organizations, which suggests they are building solutions in-house vs. using out-of-the-box software). 

Has Your Organization Increased the Use of Open Source Software Over the Last Year?

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No License Cost and Overall Cost Reduction Is Driving Adoption

This year, 53% of respondents cited reducing cost as their top reason for choosing OSS, a big increase from 37% last year (+43% YoY). Economic uncertainty continues to motivate businesses to keep costs low and predictable, and many have discovered that adopting open source is the best way to do that. Other reasons that made it into the top five:

• To reduce vendor lock-in (33%)
• Open standards and interoperability (28%)
• Stable technology with community long-term support (24%)
• To reduce development or maintenance costs (22%)

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Security, Compliance, Maintenance, and Personnel Are the Biggest Challenges

We asked respondents to assign a score between 1 and 5 to some known OSS support challenges, where 1 = not challenging and 5 = very challenging. This table shows the challenges where more than half of our survey population assigned a score above 3:

These findings make sense in light of the next question we asked: “In cases where you are using the proprietary version of open source-based software, what is keeping you from switching to the open source technology?”

The top answer, with 44%, was “professional support and maintenance.” In other words, organizations may have to pay for commercial versions that include technical support and LTS if they don’t have the internal resources (including skilled personnel) to keep up with updates and patches and meet their compliance requirements on their own. 

Want More Open Source Insights and Analysis? 

Download the full State of Open Source Report for 30+ pages of data on open source technologies: Linux distros, frameworks, databases, runtimes, DevOps tooling, and more!

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EOL Software Makes Failing a Compliance Audit 3x More Likely 

Only 14% of organizations said they failed a compliance audit last year. However, that percentage nearly tripled to 41% when we just looked at respondents who indicated that they are still using EOL software like CentOS and AngularJS.

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Confidence in Big Data Management Is Low 

We dedicated a section of the report this year to explore Big Data trends and concerns. Since large datasets are used to train AI models, it’s an exciting space to watch. What we found, interestingly, is a lack of confidence in the management of Big Data technologies. 

47% gave a score of 1 or 2 (out of 5) to describe their confidence in the administration of their Big Data stacks. These technologies are notoriously complex and skills gaps are part of the problem here — more than 75% selected “personnel proficiency and experience” or “lack of personnel” as the most challenging aspect of working with open source data technologies. 

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Final Thoughts 

On a positive note, the report contains some encouraging signs of growing open source maturity and best practices among organizations. For instance, 59% of respondents said that they scan open source software for vulnerabilities, and 35% have open source security, compliance, or governance policies. Those numbers are both up from last year’s report. Even better, 38% said that their organizations contribute to open source projects or foundations. Those contributions could be financial or in the form of code or other service to the community, but regardless, more than a third are giving back in some way. 

The State of Open Source Report is a valuable resource for anyone — from engineers to CTOs — interested in understanding how the open source landscape is changing so they can be more strategic about OSS adoption, support, and end-to-end stack management.

Hadoop has revolutionized the way enterprises process and manage massive amounts of structured and unstructured data. For organizations to harness the full potential of Big Data, an efficient Hadoop environment has never been more vital, which is where Hadoop administration comes in.

In this blog, we will unpack the complexity of Hadoop administration to help those tasked with managing Hadoop understand their options so they can stay competitive in the Big Data landscape.

Hadoop Administration Overview

What Is Hadoop Administration?

Hadoop administration encompasses the efficient management and optimization of Hadoop clusters. The goal is to configure systems to ensure high availability, performance, and security. This requires handling clusters of tens, hundreds, or even thousands of nodes, making sure systems deliver consistent value to business operations.

Key Responsibilities of a Hadoop Administrator

• Cluster setup, configuration, and installation
• Performance monitoring and resource optimization
• Account provisioning and access control
• Diagnosing and troubleshooting issues
• Managing backup and disaster recovery

Hadoop administrators are the custodians and guardians of Big Data ecosystems, working behind the scenes to keep critical operations running smoothly.

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Managing Hadoop: Key Components

Managing Hadoop effectively requires understanding its architecture, components, and add-ons. Below are the building blocks of a production-quality Hadoop cluster; anyone tasked with Hadoop administration should have expertise with configuring and monitoring these components. 

Storage Management via HDFS (Hadoop Distributed File System)

1. Primary function: Reliable storage of large data volumes across distributed systems.
2. Role of the administrator: Monitor storage utilization, manage data replication, and ensure data integrity.

Resource Management via YARN (Yet Another Resource Negotiator)

1. Primary function: Resource allocation and job scheduling across the cluster to maximize efficiency.
2. Role of the administrator: Balance resource demand across multiple jobs, users, and services to avoid bottlenecks.

Data Processing via Computation Engines (e.g. MapReduce, Spark, Tez)

1. Primary function: Provides use-case specific tools for conducting distributed analysis, transformation, and machine learning tasks on large datasets in batch or streams.
2. Role of the administrator: Monitor jobs, optimize workflows, and troubleshoot failed tasks.

Administrators must also install and maintain various tools that provide end-users and application developers with solutions specific to business requirements. This will likely include Hive for SQL-like access to data in HDFS, HBase for real-time i/o to data in HDFS, and Zeppelin for authoring and executing code adhoc.

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Challenges in Hadoop Administration

Hadoop’s power comes with its share of complexities. Administrators face several challenges, such as:

• Distributed Storage Management: Keeping track of data consistency and replication across distributed nodes.
• Resource Allocation Conflicts: Balancing YARN resources for multiple jobs without performance degradation.
• Security Risks: Preventing unauthorized access to sensitive data within the Hadoop cluster.
• Performance Bottlenecks: Optimizing job scheduling or node performance to avoid inefficiencies.

Tackling these challenges requires a proactive approach and reliable tools, which we’ll cover in the next two sections of this blog.

Best Practices for Effective Hadoop Administration

The following practices are critical for Hadoop management:

1. Monitor Cluster Health Regularly: Proactive Hadoop monitoring prevents failures before they escalate. Deploy tools like Apache Ambari to track metrics, such as node status, storage usage, and job progress.
2. Implement Backup and Recovery Plans: Ensure all mission-critical data is backed up. Use snapshots for HDFS and regularly test recovery protocols to minimize downtime.
3. Stay Updated with Versions and Patches: Hadoop releases frequent updates to address security vulnerabilities and improve functionality. Always operate on the latest stable version to prevent exposure to risks.
4. Optimize Resource Configurations: Fine-tune YARN settings, such as memory allocation and scheduler configurations, for optimal performance.
5. Strengthen Security:
   • Kerberos Authentication: Safeguard access by requiring mutual authentication between users and services.
   • Encryption: Implement Transparent Data Encryption for static data and SSL/TLS for network traffic.
   • Least Privilege Model: Limit user access to only what is essential for their role.

By adopting these practices, Hadoop administrators can maintain an optimized and secure environment.

See more best practices for Hadoop security >>

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Essential Tools for Simplifying Hadoop Administration

Modern Hadoop administration relies heavily on tools to streamline operations. Collectively, these technologies empower Hadoop administrators to manage large-scale systems efficiently while reducing manual overhead. Here are some of the most effective ones to consider deploying:

Apache Ambari

Ambari is an open source tool for provisioning, managing, and monitoring Hadoop clusters. Its intuitive dashboards make it easy to track metrics and manage alerts.

Compare Ambari vs. Cloudera Manager >>

Apache Bigtop

Bigtop is an infrastructure tool designed to help package, deploy, test, and manage various components in a Big Data cluster, like Spark, Hive, HBase, etc.

Apache Ranger

This tool enhances security by allowing centralized authorization management, including role-based access controls and audit logs. Ranger is capable of tracking events and activities across the platform, even those taking place outside of Hadoop.

Ambari Metrics Server (AMS) and Prometheus

These tools and others gather critical data on cluster health and performance, helping administrators analyze trends and act before failures occur. 

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Final Thoughts

With enterprise data continuing to grow exponentially, Hadoop administration will remain a critical skill in technology-driven industries. Emerging trends suggest stronger integration with AI/ML for predictive maintenance and hybrid cloud implementations for even greater scalability.

Mastering Hadoop administration involves more than just managing clusters; it requires breadth and depth of knowledge.  An administrator must follow and understand evolutions in the overall ecosystem to overcome challenges and continuously adapt to new needs.  It is a time-intensive job and some enterprises may find it more effective to outsource their Hadoop administration to a third party with proven Hadoop expertise like OpenLogic. This option prevents Hadoop administration from draining resources, so teams can stay focused on utilizing their Big Data for AI, operational decision-making, and other use cases that impact the business.

The end of the CentOS Linux community support lifecycle has left many organizations searching for a stable, permanent replacement for their operating systems. AlmaLinux, as an open source RHEL fork, has gained massive traction, especially among system administrators, developers, and IT decision-makers.

However, moving from CentOS to AlmaLinux, particularly in large scale deployments with thousands (or tens of thousands) of servers, is far from a plug-and-play migration. It requires careful planning, technical expertise, and meticulous execution to ensure business continuity and operational efficiency.

Below, we’ll walk you through the essential steps for a successful CentOS to AlmaLinux migration, focusing on some of the common obstacles teams may encounter during the process.

Editor’s Note: This blog was originally published on September 12, 2022 and was updated for accuracy in March 2025. 

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Why Migrate From CentOS to AlmaLinux?

Red Hat’s decision to  phase out CentOS’s stable release model to focus on CentOS Stream — which delivers more frequent updates due to its rolling release model — left enterprises craving stability and predictability. AlmaLinux emerged as one of the top CentOS alternatives due to its binary compatibility with RHEL and its steadfast support of the stable release model.

What Makes AlmaLinux a Good CentOS Alternative?

• Stability: Unlike CentOS Stream, AlmaLinux stays committed to stability, making it suitable for production environments.
• Community-Driven: Maintained by the AlmaLinux Foundation, AlmaLinux has an active and devoted community.
• Binary Compatibility: AlmaLinux mirrors RHEL, ensuring minimal disruptions when transitioning.
• Cost-Effective: Unlike RHEL, it is free but offers comparable enterprise-level reliability.

If maintaining critical business operations on a predictable, secure Linux distribution is your priority, AlmaLinux is an excellent choice.

Need More Time on CentOS? Get Long-Term Support Today

CentOS LTS from OpenLogic extends your migration runway up to five years past community EOL. Secure your deployments and stay compliant while you plan your migration.

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CentOS to AlmaLinux Migration Steps

Migrating hundreds or thousands of systems across environments involves more than swapping repositories. Here we break down the steps involved before, during, and after you migrate. 

1. System and Application Compatibility

Conduct a detailed inventory of your CentOS systems. Identify:

• Applications, databases, and processes tied to your OS.
• Third-party packages and dependencies for compatibility checks.
• Proprietary or custom-built tools that may have version-specific constraints.

2. Hardware Requirements

Ensure your infrastructure supports AlmaLinux versions. Legacy hardware used with CentOS 6 or 7 may need replacements due to incompatibilities with newer kernels.

3. Backup and Rollback Strategies

Before initiating migration, confirm backup systems are in place and test their validity:

• Perform full system snapshots and data backups.
• Ensure rollback plans can reinstate current setups in case the transition introduces issues.

4. Testing and Validation

Pilot migrations in a staging environment, simulating production operations. Address migration errors or failures before implementing across critical systems.

5. Choosing the Right Migration Path

Select between in-place migration or new environment deployment. Each approach has distinct tradeoffs:

• In-place migrations: Best for physical hardware when no substantial downtime is allowed. However, it carries risks of disruptions if interrupted.
• New environment deployments: Ideal for virtualized or cloud systems. This approach minimizes disruptions but requires additional hardware or temporary instances.

If you are on physical hardware, migrating in place may be the easiest (and cheapest) route because it does not require more systems. In this scenario, it’s important to make sure that connections are stable, and the machine will be available the whole time, because the system could end up in an unrecoverable state if the script gets interrupted. If that were to happen, you would need a rescue disk and some manual work to get back to functionality. If you can’t take a system out of use to rebuild, then running the migration may be the best option.

However, if you have spare hardware or are on a virtualized infrastructure, it would be preferable (and safer) to build a new system to your specifications, migrate the apps and data over, and then replace the old system with the new one.

Carefully weigh these considerations to tailor your CentOS migration strategy for your unique needs. Now let’s look at some actual migration paths:

CentOS 6 to AlmaLinux Migration Path

Since there is no direct migration path from Centos 6 to AlmaLinux (AlmaLinux starts at 8.4), you must be on CentOS 8.4 at least in order to migrate.

Two possible approaches — the first would be to upgrade from 6 to 7 to 8, and then migrate your data. However, this would be time-consuming and risky because of all the package changes between major versions.

The second (and more advisable) approach is to build a new machine prior to migrating. Best case scenario: All required third-party software has a new version, and data can be safely upgraded. Worst case: There is software that depends on CentOS libraries. If the latter is true, you would need to either find alternatives, or identify a way to run the software anyway. Fortunately, containerization makes it relatively easy nowadays to run older versions of software on newer systems, or even on completely different distributions.

CentOS 7 to AlmaLinux Migration Path

The migration path for CentOS 7 is virtually identical to CentOS 6, but a little easier because CentOS 7 already uses systemd for service management, whereas CentOS 6 uses legacy SysV init scripts. There are a few other changes, but that is a major one that makes upgrading from 7 less complex than 6.

For detailed instructions on how to migrate from CentOS 7 to AlmaLinux 9 (the current version), take a look at this blog on CentOS to Rocky Linux migration and simply ignore the steps that are specific to converting from the latest version of Alma to the latest version of Rocky. 

CentOS 8 to AlmaLinux Migration Path

Since CentOS 8 and AlmaLinux 8 are nearly identical, this is a relatively painless migration. Minor changes only; the CentOS repos are swapped out for AlmaLinux repos, and a handful of packages (mostly branding packages) are replaced.

CentOS Stream to AlmaLinux Migration 

The almalinux-deploy.sh script automates the process of converting a CentOS Stream system to AlmaLinux 8. It creates the repo files for AlmaLinux, replaces branded packages, and then performs a distro-sync to sync up the system to the current version of the packages in the repos.

Note: The migration will work with CentOS Stream, but some packages may be a little newer than the current AlmaLinux packages, so it downgrades packages using distro-sync to match the current version in the repos.

6. Run Pilot Migrations

Select non-critical systems to test the migration process:

• Execute the switch to AlmaLinux.
• Validate applications and services post-migration.
• Modify or replace failed dependencies.

Based on test results, refine the migration plan for production systems.

7. Full-Scale Deployment

Once confident, proceed with rolling out migrations across production systems:

• Schedule migrations during downtime or maintenance windows.
• Maintain dedicated technical teams ready to handle interruptions on the fly.

Implement migrations in phases to minimize potential disruptions and rollback issues.

8. Post-Migration Testing

Run complete QA procedures post-migration:

• Verify application functionality and security.
• Monitor system logs for errors or warnings.
• Confirm user access and permissions are intact.

Fix issues before declaring the migration successful.

9. Ongoing Maintenance

Post-migration upkeep ensures systems remain secure and scalable:

• Enable AlmaLinux repos for regular OS updates.
• Schedule periodic system health checks.
• Establish monitoring and alert systems for performance optimization.

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Other Considerations

The only official migration script is almalinux-deploy.sh, provided by AlmaLinux itself. This script will not only migrate CentOS 8 to AlmaLinux, but also Oracle Linux 8, RHEL 8, Rocky Linux 8, Virtuozzo Linux (VZLinux) 8, and CentOS Stream, along with the version 9 variants. There may be others out there, but you should proceed with caution, and verify the script does what is wanted.

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Final Thoughts

Executing a CentOS to AlmaLinux migration is not without its challenges, but with proper planning and resource allocation, your organization can smoothly transition to a stable and future-proof Linux distribution. While migration may feel daunting, following structured processes ensures minimal risk while maximizing uptime for your business.

Lastly, if you’re navigating a large-scale migration and lack internal expertise, OpenLogic’s Linux experts can alleviate the strain. We can offer guidance or our Professional Services team can perform the migration itself. Once you have successfully migrated, we also offer 24/7 AlmaLinux support. 

Puppet Enterprise Advanced embeds security remediation into the heart of infrastructure automation unlocking seamless DevSecOps collaboration.

MINNEAPOLIS, APRIL 22, 2025 — Perforce Software, the DevOps company for global teams seeking AI innovation at scale, today announced the release of its latest platform update for Puppet Enterprise Advanced, designed to streamline DevSecOps practices and fortify enterprise security postures. This release incorporates more advanced and proactive remediation options, allowing organizations to accelerate their response to security vulnerabilities by fostering greater collaboration between platform and security teams.

Today’s threat landscape is evolving rapidly in both agility and sophistication thanks in part to the misdirected power of AI. However, the divide between infrastructure management and security operations creates a critical bottleneck to a swift response. A 2024 study by Statista reported that the average age of cyber vulnerabilities is 229 days leaving companies and their customers vulnerable to security breaches and being found to be non-compliant. Still, companies trying to solve this problem are being challenged by rapid scaling of infrastructure, inefficient operational practices, and an ongoing global cyber skills shortage.

Puppet’s latest platform update enables enterprises to rapidly address known vulnerabilities by integrating security remediation within core infrastructure workflows to accelerate responsiveness to identified threats. Embedding this capability into established processes gives operations and security teams a shared understanding of their security posture and automates critical remediation tasks to eliminate inefficient cross-functional handoffs.

“Vulnerabilities continue to increase, with around 40,000 known vulnerabilities in 2024. Because of this explosion in vulnerabilities, the mean time to remediate continues to increase, leaving companies extremely vulnerable to attacks. Enterprises must combat this by integrating security with infrastructure automation to shorten the vulnerability remediation cycle,” said Tzvika Shahaf, Vice President of Product Management at Perforce. “Our new release empowers organizations to unlock a collaborative DevSecOps environment by shrinking the opportunity window for attackers. In future Puppet releases, the pace will quicken further with human-in-the-loop, AI-driven automation.”

Puppet enables the platform team to support security initiatives to improve resiliency and reduce the MTTR (mean time to remediate). Lowering risk, improving efficiency, and reducing cost is accomplished by providing:

• Remediation of known vulnerabilities is streamlined by integration with leading security scanners. Tenable Nessus is included out-of-the box and other popular scanners are available via an extensible architecture and open API framework. Self-service workflows and cross-functional visibility break down siloes to eliminate efficiency bottlenecks and accelerate resolution.
• Patching workflows are managed directly in Puppet and benefit from an intuitive GUI interface. Support for maintenance and blackout windows ensures less business interruption while dynamic patching groups provide the operational efficiency necessary when managing extensive hybrid estates.
• Desired state enforcement ensures configurations remain continuously aligned with security policies. Remediation is automatic when discrepancies are identified along with the generation of documentation needed by auditors.
• Automatic alignment with popular security baselines via pre-built, reusable policy as code (PaC) modules ensuring the latest best practices are always being applied.

Hadoop security presents unique challenges due to its distributed architecture and reliance on third-party services, making it particularly vulnerable to attacks if not properly secured. 

In this blog, our Hadoop expert explains the five core principles of Hadoop security and shares best practices and recommended tools to reduce your attack surface and protect data from being accessed by bad actors.

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Understanding the Challenges of Hadoop Security

Hadoop implementations are subject to all of the same security concerns that apply to modern-day distributed systems. These challenges differ from monolithic applications that access traditional databases.

Some examples:

• Data in Hadoop is distributed across multiple nodes. Without proper encryption of data at rest, a single compromised node can allow a bad actor to lift parts of files and reveal sensitive data or change values.
   
• Services in Hadoop typically run on separate nodes and use the network to communicate with one another. Without proper encryption of data in motion, the system is vulnerable to man-in-the-middle attacks. A bad actor could intercept sensitive data or inject malicious code.

What makes Hadoop uniquely complex is the number of third-party services in the ecosystem that are commonly used to meet specific needs. Many of the services used within the Hadoop ecosystem can also be run independently outside of Hadoop; therefore, they have their own standalone security implementations and default configurations.

It is important to have an overarching security strategy for a Hadoop cluster, so the individual services can be configured to leverage a single cohesive approach to common security issues. Otherwise, the overall system is vulnerable to data breaches and service disruptions.  

Fortunately, there are some core configurations, best practices, and recommended tooling that can help drive a solid strategy that results in a hardened Hadoop implementation.

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Core Principles of Hadoop Security

Early Hadoop design and development focused on solving problems with high availability and scalability of data storage and analytics; therefore, security was not a bedrock component. Although security wasn’t an early concern in the problem domain, it must be a foundational consideration for all production Hadoop implementations.

Here are five crucial areas to focus on in order to ensure coverage of basic information security (i.e. confidentiality, integrity, and availability):

1. Authentication: This ensures only known and registered users and services can access the Hadoop cluster.
2. Authorization: This requires authenticated users (and services) to be explicitly granted access to applications, data, and services that are managed by the Hadoop cluster.
3. Encryption: This protects data stored (at rest) in the system, as well as data being passed around (in motion) the system from unauthorized access.
4. Isolation: This prevents a single user, service, or group from consuming resources on the cluster in a way that would compromise performance for other users (and services) sharing the system or jeopardize the availability of the system as a whole.
5. Remediation: This is a combination of tooling and processes that monitor the overall system and take action to identify and address any threats to security, availability, or stability.

Ready to Open Source Your Big Data Management?

This white paper explains how enterprises can reduce costs and have more data sovereignty by open sourcing their Big Data infrastructure instead of paying for a proprietary solution like the Cloudera Data Platform.

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Hadoop Security Best Practices

Security in Hadoop must be implemented in layers to create a robust strategy that guarantees confidentiality, integrity, and system availability. Each layer potentially protects the whole system, but it should be viewed as a hurdle for bad actors, rather than a wall. Therefore, it is key to stand up each hurdle in an effort to repel attacks and minimize the impacts of a breach.  

Here are some best practices to include when securing a production Hadoop cluster:

1. Change Default Passwords and Communication Ports: Many of the services that run within the Hadoop ecosystem have default service principal and password combinations as well as default communication ports for accessing information managed by the service. These are readily accessible via the service documentation and source code repository and must be changed to prevent unauthorized access to the system.
    
2. Use a Private Network: The Hadoop cluster and related services should all be on a private network that is not accessible from the internet (with a VPN implemented to enable remote private access if necessary). If this is not possible, then employ firewalls, reverse proxies, and secure gateway techniques to hide details of the implementation from potential bad actors.
    
3. Use the Kerberos Authentication Protocol: This has been widely adopted for good reason and brings many benefits including:
   1. Reciprocal/mutual authentication that requires both a client and a server to verify each other’s identity before allowing entry. This reduces the attack surface for man-in-the-middle attacks.
       
   2. Single sign-on (SSO) that gets a user access to all services without entering their principal and password multiple times. Once the credential is entered, an expiring ticket is used for subsequent requests. This reduces the chance of a hijacked session.
       
4. Encryption: Use the Transparent Data Encryption (TDE) built into the Hadoop Distributed File System (HDFS) to encrypt data that is stored (at rest) in Hadoop, and configure SSL/TLS to encrypt traffic (in motion) that is communicated between clients and services within the system.
    
5. Manage Permissions Through Role-Based Access Controls (RBAC): This allows for more organized, policy-based access to data, applications, and services through categories of use cases, making it easier to audit which users and services have access to various resources or actions in the system. It also allows quick confident maintenance of those privileges by adding and removing users from categories of access, rather than maintaining separate Access Control Lists per individual.
    
6. Adhere to the Practice of Principle of Least Privilege (PoLP): Strive to give users and services the minimum access necessary to perform a job function. This is intended to limit the individual user’s access, but it is equally important to limit the attack surface should any user accounts be compromised.

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Hadoop Security Tools

There are a handful of tools commonly used to implement or manage aspects of Hadoop security. Kerberos and TDE, mentioned above, are fundamental. This section will highlight other tools that enable Hadoop admins to follow the principles and best practices outlined in the previous section.

YARN Capacity Scheduler

This tool, which is built into Hadoop, should be used to assign user and service quotas to create resource isolation. This is a popular technique for reducing the chances that a runaway process can consume too many resources on the cluster, which supports system availability and stability.

Apache Ranger and Apache Sentry

Apache Ranger and its deprecated predecessor, Apache Sentry, are used to control access to the various services and data sources in the Hadoop ecosystem. These access controls allow permissions to be defined at a granular level like table, row, and column access in databases like Hive or Impala, as well as file-level control in components like HDFS. 

Apache Ranger also adds features above and beyond what was previously available in Apache Sentry. For example, it provides audit logging that captures all data access events. Going further, it supports sending that information in real-time for monitoring and analysis in tools like Elasticsearch, OpenSearch, and Grafana. This makes Apache Ranger a linchpin in threat detection that drives remediation of attack vectors.

Apache Knox

Apache Knox, which is becoming more prominent in Hadoop implementations, assists with network-level security of a Hadoop deployment. Knox is a reverse proxy that serves as an application and API gateway. This makes it the single point of entry for managing external traffic in and out of the system. A tool like this is essential to securing a Hadoop cluster that requires connections from the internet.

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Final Thoughts

Having the right tools and practices in place is the first step toward securing your Hadoop implementation. In today’s threat climate, there is no shortage of malicious parties looking to steal to sensitive data, so it’s imperative to have a strong Hadoop security strategy in place to disincentivize and rebuff attacks. 

The new offering provides expert-backed services, security guidance, and tuning support to help organizations confidently and efficiently run Kafka at scale without vendor lock-in.

MINNEAPOLIS, April 15, 2025 /PRNewswire/ — Perforce Software, the DevOps company for global teams seeking AI innovation at scale, today announced the launch of the Kafka Service Bundle, a new offering that provides enterprises with managed open source Apache Kafka at a fraction of the cost of traditional managed providers.

More than 80% of the Fortune 100 rely on Kafka for their streaming data pipelines. The distributed event streaming platform, which can process and store large volumes of data and deliver real-time analytics, is widely used across industries including banking, healthcare, and retail. The Kafka Service Bundle helps organizations seamlessly shift from proprietary Kafka platforms to customized, open source Kafka implementations — allowing them to leverage the power, reliability, and scalability of Kafka while significantly reducing operational expenses.

“Kafka is a notoriously complex technology that demands a high level of expertise, but organizations are struggling to pay the premiums charged by managed Kafka providers. They need a more cost-efficient way to securely scale their data infrastructure — and tailor it to meet their needs,” said Matthew Weier O’Phinney, Principal Product Manager at Perforce OpenLogic. “With the Kafka Service Bundle, we’re enabling teams to keep their Kafka environments running smoothly — without getting locked in to an out-of-the-box solution.”

With economic headwinds forcing many enterprises to find creative ways to cut IT infrastructure costs, there is an emerging trend of companies pursuing free open source options — especially in cases where the open source alternative matches a commercial solution in functionality, reliability, and performance. That trend was apparent in a recent survey from Perforce, where they reported a 43% year-over- year increase in organizations choosing open source due to cost-savings.

“A global digital media company was struggling to deliver real-time consumer insights to their retail clients at the point of sale due to a brittle Kafka implementation. We helped them build a more resilient, efficient data pipeline by tuning their clusters and modernizing their monitoring strategy,” said Chelsea Teplitzky, Managing Director at Perforce Software. “That’s exactly the kind of real-world impact we’re aiming to deliver with the Kafka Service Bundle — giving enterprises the expert support they need to achieve their business goals with open source.”