Apache Ambari vs. Cloudera Manager

In this blog, we'll take a close look at the two leading cluster administration tools in the Hadoop big data space: Apache Ambari vs. Cloudera Manager. We’ll spotlight differentiators that are essential to understand before establishing a new big data cluster or charting the future course for an organization with a growing big data platform.   

Note: This Ambari vs. Cloudera Manager comparison reflects features coming in Apache Ambari version 3, which is due out this month and includes distinct changes to the deployment model.

Comparing Apache Ambari vs. Cloudera Manager

Apache Ambari and Cloudera Manager have similar functionality when it comes to how they provision, manage, and monitor Hadoop clusters. The main difference between Ambari vs. Cloudera Manager is that Ambari is free, open source software and Cloudera Manager is proprietary software that requires a paid subscription. 

What Is Apache Ambari?

Apache Ambari is an administrative application offered through the Apache Software Foundation. It has a web-based user interface and a programmatic REST API that allows organizations to provision, manage, and administer Hadoop clusters and associated services. 

Apache Ambari is fully open source and available for free download and implementation under the Apache2 license.

What Is Cloudera Manager?

Cloudera Manager is the administrative application for the Cloudera Data Platform (CDP). It has a web-based user interface and a programmatic API, and is used to provision, configure, manage, and monitor CDP-based Hadoop clusters and associated services. 

Although CDP has foundations in open source, it is a proprietary licensed ecosystem that requires a paid subscription to host and manage data.

Ambari vs. Cloudera Manager: Key Differences

By and large, these technologies have a comparable and competitive feature set for creating and maintaining data clusters and services. However, there are a couple of distinct architectural differences between Ambari and Cloudera Manager that drive implementation choices and impact future flexibility. In this section, we’ll look at how they differ in these key areas.

Multi-Tenancy

Cloudera Manager supports the creation and management of multiple clusters within a single user interface (multi-tenancy), whereas Apache Ambari requires an administrative server instance for each cluster. 

A multi-tenant approach has pros and cons. Having everything in one place is convenient for advertising and maintaining a single location for all users to access information and services associated with all clusters. However, it has a higher risk of human error, and it suffers from the common shortcomings of shared monolithic environments.

Cloudera Manager's "one-stop shop" model is particularly desirable for data administrators in large organizations that have many clusters used to fence data and services. End-user muscle memory allows them to quickly navigate to a single dashboard that opens access to an array of data collections and gets them to the right place with less effort.

However, the convenience of logical security comes with some inherent risks. The ease of access and common interface can suffer from human error related to scrolling blindness, parallax effect, and other usability issues. In multi-tenant systems, a user can more easily wreak havoc, by inadvertently taking action on a cluster by selecting an option while in the wrong context. 

This is particularly risky in an environment where development, test, and/or production environments share a common interface. For example, a user could restart a production cluster, while intending to restart a development cluster. Multi-tenancy across those boundaries is an ill-advised practice, but avoiding it diminishes the value of multi-tenancy in many or most organizations.

To a lesser extent, multi-tenancy can be seen as a means to achieve cost savings for system administration. Yet modern deployment models using virtualization, containerization, and DevSecOps practices largely counter the benefits of single monolithic systems and tip the scales toward smaller, more controlled units via infrastructure as code. Maintaining multiple purpose-driven clusters also allows for more traditional physical security boundaries, like firewalls, as well as horizontal scaling methods to managing performance and availability concerns.

Deployment

Apache Ambari has moved away from using Hortonworks Data Platform stack RPM-based service deployments for clusters. It now uses Apache BigTop RPMs to standardize service deployments. On the other hand, Cloudera Manager uses a proprietary binary distribution format called Parcels to deploy a curated service stack.

Apache Ambari also uses what is called Ambari Management Packs (Mpacks) to bundle and describe groups of services that are installed and associated with a cluster. This decouples features and functionality of the Apache Ambari administrative interface from the deployment and management of the services stack, thus allowing the services stack to evolve independently of the Apache Ambari product release. This separation, along with the use of an open standard for producing and including service binaries, creates freedom and flexibility enabling organizations to create, update, and upgrade their own curated service stack based on domain-specific application requirements.

Using Apache BigTop deployment binaries wrapped in an Mpack descriptor has the added benefit of repeatable standardized deployments across Apache Ambari managed clusters. It also opens the door to sharing standard service binaries with a wide range of other big data tools and platforms that support Apache BigTop RPMs.

Cloudera Manager service stacks are curated, built, and distributed by a vendor using a proprietary format. Organizations cannot define and reproduce their own stack. Updates and upgrades are provided by a vendor (Cloudera) on their timeline, and the service binaries are not sharable across other big data tools and platforms.

When to Use Ambari vs. Cloudera Manager

Before deciding which Hadoop orchestration tool to use for enterprise deployment, there are a few considerations that teams should keep in mind.

Apache Ambari Use Cases

Organizations that want to establish or maintain flexibility in their big data platform, and those that want to leverage emerging features and standards in the space should consider Apache Ambari as their administrative console and inception point for their big data stack.

Apache Ambari is the only Hadoop big data cluster administration tool on the market that uses standardized service deployments that users can bundle, define, test, and deploy into an organization-specific curated stack. This makes it the only option for organizations that want or need the flexibility to control the versions of software in the services stack. This capability is essential to adopting features of the underpinning services as they are released. Perhaps more importantly, it also makes it possible to apply patches for critical security defects and CVEs as they become available.

The real value of a big data stack is the outcomes of the analysis. It is what you do with the data, rather than the data itself. Organizations leverage the big data stack to write domain-specific applications that inform actions they should take. Those application developers frequently need the latest features available for services running in the big data stack in order to make the most of the data. It's also not unusual for those developers to need defects corrected in the software, including CVEs. With Ambari's unique deployment scheme, organizations are better positioned to evolve their platform to meet the demands of both development and security teams, who can apply these changes as soon as they are released by the community. 

Cloudera Manager Use Cases

For organizations that have implemented a Cloudera Data stack and intend to stay with that platform, choosing Cloudera Manager is likely a foregone conclusion. The Cloudera Data Platform is a tightly controlled environment that is intended to be used as a single holistic unit. Adding the Apache Ambari administrative interface and maintaining that connectivity alongside other Cloudera services would likely prove challenging and provide little value. Likewise, customers that host their data with Cloudera have little reason to use anything else. In fact, there is likely even less flexibility in that scenario.

In all practicality, adopting the Cloudera managed big data stack is an all-or-nothing proposition. This may very well be a desirable option for organizations that want an out-of-the-box solution that is tightly managed by a vendor. Cloudera Manager is best suited for organizations that have little concern with cost optimization, no need for the latest features of individual services, and no requirements driving tight management or control of security vulnerability patching.

Final Thoughts 

The really interesting conversations around Apache Ambari vs. Cloudera Manager percolate from developing strategies for transitioning existing data clusters from one of these administrative tools to the other. Depending on the environment, some organizations have to migrate data; others may be able to conduct a software cut-over, where the data remains in-place and the new administrative interface is trained to work with it.

When using a proprietary software like Cloudera Manager, you pay to offload some risk, but you lose a fair amount of control. With an open source offering like Ambari, you are investing in a broader community of interest and a mindset. This results in access to expertise and the competence to leverage a number of freely available open source technologies — giving you the flexibility to create innovative solutions that meet your demands and evolve on your timeline.

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Additional Resources

• Blog - A Second Wind for Apache Ambari
• Blog - Spark vs. Hadoop: Key Differences and Use Cases
• Blog - Are You Locked In With Commercial Open Source Software?
• Blog - Proprietary vs. Open Source
• Resource - Open Source Stack Builder