Weighing the Value of Apache Hadoop vs. Cloudera

As the Big Data landscape has changed, comparing Apache Hadoop vs. Cloudera and their commercial platform is a worthwhile exercise. Do enterprise teams still need Cloudera for their Big Data stack management or can they save by independently managing their Apache Hadoop implementation?

In this blog, we'll take a close look at the value of the Cloudera platform's software bundle, proprietary tools, and cloud-hosting services and compare it to open source alternatives that may be appealing for organizations rethinking their Big Data strategy.

Note: In this blog, references to the Cloudera platform are meant to encompass both the Cloudera Data Platform (CDP) and the legacy product, Cloudera Distribution of Hadoop (CDH).

Apache Hadoop vs. Cloudera Overview

Apache Hadoop is a free, open source data-processing technology that uses a network of computers to solve large data computation via the MapReduce programming model. Cloudera offers a commercial, Hadoop-based platform that is available via paid subscription.

The Cloudera platform is based on Apache Hadoop and various other software packages that, by and large, are part of the broader Apache Hadoop ecosystem. Therefore, many of the features and functions of Cloudera's platform are available for free via the collection of those foundational open source software packages. 

When customers pay for a Cloudera subscription, they are essentially paying for:

• A curated bundle of the open source software packages and specific versions that have been validated and proven to work together.
• A couple of proprietary (not open source) applications that provide conveniences intended to help adopters manage an implementation of these disparate open source software packages.
• A hosted managed services provider that unites it all in a controlled environment with the promise of stability, availability, and carefree maintenance.

While valuable for some enterprise use cases, these benefits come at a price — particularly the last one, as cloud migrations can be expensive. Because the Big Data landscape is continuously evolving with new solutions coming on the market all the time, it is a good practice to regularly evaluate the return on investment of those features against the cost of managing an equivalent open source stack. 

Apache Hadoop vs. Cloudera: Tooling Comparison

Many enterprises are reevaluating being locked in vs. having flexibility, especially now that more innovative and impactful open source technologies are available. Specifically, there are a couple of foundational areas where Apache Hadoop has made considerable advancements compared to what you get with Cloudera. 

Here's a table showing a side-by-side comparison of what tools come with Cloudera vs. a fully open source Hadoop stack.

*indicates proprietary tool

 As you can see, for many functions, the tooling is identical and there are only two proprietary technologies (Cloudera Manager and Cloudera Navigator). Let's dig to some of the areas where there are differences and consider why those might matter. 

Cluster Administration: Cloudera Manager vs. Ambari

Cloudera Manager handles Hadoop administration 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.

Apache Hadoop implementors use Apache Ambari (a project started by Hortonworks, which was acquired by Cloudera in 2019) to accomplish what is offered through Cloudera Manager on CDP Hadoop implementations. Apache Ambari has a web-based user interface and a programmatic REST API that allows organizations to provision, manage, and administer Hadoop clusters and associated services.

You can read my previous blog comparing Apache Ambari vs Cloudera Manager for more nuanced details about these two tools. 

Metadata Management and Data Governance: Cloudera Navigator vs. Apache Atlas

Cloudera Navigator handles data governance. It offers a wide range of features for auditing and compliance, from organization policy creation and tracking to regulatory requirements like GDPR and HIPPA. It also includes data lineage tracking to look back upon data transformation and evolution, as well as metadata management for tagging and categorizing data to assist in searching and filtering.

Apache Hadoop implementors use Apache Atlas (also originally developed by Hortonworks) to implement data governance and metadata management. Cloudera Navigator is only applicable to CDP, whereas Apache Atlas works across a broad range of Hadoop distributions and data ecosystems. It is extensible and integrates with other packages, like Apache Hive and Apache HBase.

Apache Atlas logs creation, modification, access, and lineage information about each data asset. It tracks who has accessed or modified data to provide an audit trail for compliance and monitoring purposes. Policies can be defined in Atlas to manage role-based access control (RBAC), attribute-based access control (ABAC), and data masking. To enforce these policies, Atlas integrates with Apache Ranger (another open source package in the Hadoop ecosystem).

Cluster Execution Services: Oozie vs. Airflow

At a time when more modern organizations are moving toward Apache Airflow for workflow, Cloudera is still shipping with, and relying on, Apache Oozie. Apache Oozie workflows are tied to the Hadoop ecosystem and require unwieldy XML-based definitions. In contrast, Apache Airflow is a more modern, flexible, and scalable workflow and data pipeline management tool that integrates well with cloud services and various systems beyond Hadoop. It has a friendly user interface, a strong community, and advanced error handling. 

Cluster Security Services: Navigator & Sentry vs. Atlas & Ranger 

Modern Apache Hadoop implementations use a combination of Apache Atlas and Apache Ranger. Both of these products achieve significant improvements over the legacy Navigator and Sentry. Atlas will be covered again later when highlighting data governance. Apache Ranger has a more user-friendly web-based interface that makes it easier to create and manage security policies. Unlike Sentry, Ranger includes built-in robust auditing capabilities for tracking events and activities across the platform, even outside of Hadoop proper.

Read more about Hadoop security >>

To be fair, Cloudera is migrating to these improved options as well, but they are not there yet — leaving CDP implementers saddled with the complexity of a combined solution but unable to benefit from the full set of new features.

Cloudera's Cloud-Hosting Environment and Managed Services

Measuring the value of where the infrastructure resides will likely be more of a policy question for most organizations. Most organizations have a preference or a requirement that dictates whether they host services in public, private, on-premises, or hybrid clouds. So the real assessment here lies more in the value aligned with the managed services offered by Cloudera. For organizations that are not required to manage and own their own infrastructure, and don't mind paying for these managed services, this may tip the scales in Cloudera's favor. 

However, organizations that don't want to be forced to the cloud should consider whether they have the talent, motivation, and capacity to own and maintain an Apache Hadoop implementation. The maturity of the Hadoop ecosystem and the availability of standardized cloud resources make this a viable alternative to Cloudera — but only if you have the internal resources or a partner like OpenLogic with deep Apache Hadoop expertise.

Final Thoughts

There was a time when it was easier to associate a clear value for the dollar spend on Cloudera. They were pioneers in Big Data and offered the first commercial bundle of Hadoop. They were the Hadoop provider for many of the Fortune 500 firms. The Cloudera Platform could speed time to market, providing a clear path to a stable Big Data environment that allowed implementers to focus on creating domain-specific applications that leveraged their data — rather than juggling between managing a data platform and making use of their data.

However, nearly two decades have passed since the first incarnation of Hadoop. Cloudera has been involved for over 15 years, and a lot has changed. Hadoop has matured dramatically, and the supporting ecosystem has grown. New open source solutions are being developed all the time, as well as new commercial offerings around Big Data services and support. While there is still an appetite for hands-off, fully managed Big Data platforms like the one that Cloudera offers, the price has driven demand for lower-cost alternatives. For some organizations, using Apache Hadoop and avoiding a costly cloud migration is priceless.  

Additional Resources

• Solution Datasheet - Hadoop Service Bundle 
• Guide - Hadoop and Big Data
• Webinar - Is It Time to Open Source Your Big Data Management?
• Blog - Hadoop Monitoring: Tools, Metrics, and Best Practices
• Blog - Introducing the Hadoop Service Bundle From OpenLogic
• Blog - Spark vs. Hadoop: Key Differences and Use Cases
• Blog - Hadoop Performance Boosters